Analysis of consumer behaviour in food consumption

University of Castilla-La Mancha

PhD in Economics and Business

Doctoral Thesis

Analysis of consumer behaviour in food consumption decision processes: Evidence found in fast food

restaurants in Mexico.

Presented by Héctor Hugo Pérez Villarreal

Supervisors: Dr. María Pilar Martínez Ruiz

Dr. Alicia Izquierdo Yusta

October 2019

To all the people who ever believed in me.

Acknowledgement

I would first like to thank my thesis advisors Pilar and Alicia, for their

motivation, knowledge and support this Doctoral Thesis. Additionally,

thank you so much for teaching me to see life differently.

I would also like to thank the experts who were involved in the process for

this research project: Daría and Igor, with their passionate participation

and input for the first study of this Thesis.

Likewise, I would like to thank Carlos and Lisa for helping me with some

ideas to continue this research. Thanks for their hospitality in my stay of

research at Kedge Business School.

I would like to express my gratitude to UPAEP University and Fondo

Concursable with the financial aid in these five years of this program.

Also, to Becas Santander to believe in my project and help me in the same

way.

Finally, I must express my very profound gratitude to my parents, brother

and sisters for providing me with unfailing support and continuous

encouragement throughout my years of study. This accomplishment would

not have been possible without them.

Thank you.

“If you want to go fast, go alone. If you want to go far, go together.”

African Proverb

Index

Chapter 1. Introduction ................................................................................................ 1

1.1 Introduction ................................................................................................... 3

1.2 Justification ................................................................................................... 5

1.3 Objectives ..................................................................................................... 9

1.4 Doctoral Thesis Organization ..................................................................... 11

1.5 References ................................................................................................... 13

Chapter 2. Identifying research topics in marketing science along the past decade: a content analysis .......................................................................................................... 23

2.1 Introduction ................................................................................................. 27

2.2 Literature Review ....................................................................................... 31

2.3 Methodology ............................................................................................... 33

2.3.1 Data collection ........................................................................... 34

2.3.2 Properties of the dataset ............................................................. 40

2.3.3 Multivariate methods (CA and MFACT) .................................. 42

2.3.3.1 Types of results .................................................................. 43

2.3.4 Characteristic words and abstracts ............................................ 44

2.3.5 Statistical software ..................................................................... 46

2.4 Results ......................................................................................................... 46

2.4.1 Glossary of most frequent terms ................................................ 46

2.4.2 Most relevant topics and its related abstracts ............................ 50

2.4.3 Chronological evolution ............................................................ 57

2.4.4 How has the vocabulary evolved over time? ............................. 58

2.5 Discussion and Limitations ......................................................................... 63

2.6 References ................................................................................................... 66

Chapter 3. Food values, benefits and their influence on attitudes and intention to buy hamburgers: Evidence obtained in Mexico ................................................................ 71

3.1 Introduction ................................................................................................. 75

3.2 Conceptual Framework ............................................................................... 78

3.2.1 Food values and benefits ............................................................ 78

3.2.2. Attitudes and intention .............................................................. 81

3.2.3. Hypotheses ................................................................................ 83

3.3 Methodology ............................................................................................... 86

3.4 Analysis ....................................................................................................... 87

3.5 Conclusions ................................................................................................. 94

3.6 References ................................................................................................... 97

Chapter 4. Testing Model of Purchase Intention for Fast Food in Mexico: How do Consumers React to Food Values, Positive Anticipated Emotions, Attitude toward the Brand, and Attitude toward Eating Hamburgers? ..................................................... 113

4.1 Introduction ............................................................................................... 117

4.1.1 Attitudes in consumer behavior ............................................... 119

4.1.2 Purchase intention .................................................................... 121

4.1.3 Food values .............................................................................. 122

4.1.4 Anticipated emotions ............................................................... 124

4.2 Materials and Methods .............................................................................. 127

4.2.1 Data collection ......................................................................... 128

4.2.2 Statistics analysis ..................................................................... 129

4.2.3 Questionnaire development ..................................................... 129

4.3 Results ....................................................................................................... 133

4.4 Discussion ................................................................................................. 140

4.4.1 Limitations and future orientations .......................................... 142

4.5 Conclusions ............................................................................................... 142

4.6 References ................................................................................................. 144

Chapter 5. Discussions ............................................................................................. 159

5.1 Discussions ............................................................................................... 161

5.2 Business implications ............................................................................... 166

5.3 Future lines of study ................................................................................. 167

Appendices ............................................................................................................... 169

Appendice 1. Survey A ............................................................................................ 171

Appendice 2. Survey B ............................................................................................. 177

Appendice 3. Publications ........................................................................................ 183

Appendice 4. Impact factor ...................................................................................... 221

Table index

Table 2.1 Impact Factor JCR Marketing Category .................................................... 29

Table 2.2 JMR Articles by Issue and Year ................................................................. 36

Table 2.3 JM Articles by Issue and Year ................................................................... 38

Table 2.4 Descriptive Statistics of the Dataset under Analysis .................................. 40

Table 2.5 List of the 25 Most Frequent Terms ........................................................... 48

Table 2.6 Main Topic ................................................................................................. 53

Table 2.7 Distribution of Abstracts/Words ................................................................ 56

Table 2.8 Eigenvalues for First Five Components ..................................................... 58

Table 2.9 Characteristic Words by Period ................................................................. 61

Table 3.1 Technical details of the research ................................................................ 87

Table 3.2 Sample characteristics ................................................................................ 88

Table 3.3 Construct reliability and validity ................................................................ 89

Table 3.4 Discriminant validity .................................................................................. 90

Table 3.5 Path coefficients ......................................................................................... 90

Table 3.6 Variables and measure ............................................................................. 110

Table 4.1 Technical details ....................................................................................... 128

Table 4.2 Questionnaire sections ............................................................................. 130

Table 4.3 Validity testing ......................................................................................... 134

Table 4.4 Association testing ................................................................................... 135

Table 4.5 Hypothesis testing and path coefficients .................................................. 137

Figure Index

Figure 1.1 Doctoral Thesis Organization ................................................................... 12

Figure 2.1 Five-step methodology applied to this research ....................................... 33

Figure 2.2 Published articles in JMR and JM by country, from 2005 to 2014 .......... 34

Figure 2.3 Published articles in JMR and JM by year, from 2005 to 2014 ................ 35

Figure 2.4 Most contributory abstracts / words (CA) ................................................ 51

Figure 2.5 Visual representation of years and words (MFACT) ................................ 60

Figure 2.6 Periods of evolution for the vocabulary in the first MFACT plane .......... 62

Figure 3.1 Model development .................................................................................. 86

Figure 3.2 Structural model ........................................................................................ 91

Figure 4.1 Model development ................................................................................ 127

Figure 4.2 PLS analysis results ................................................................................ 139

Chapter 1. Introduction


1.1 Introduction

The food sector is one of the most important economic areas in the world (Gerbens-

Leenes, Nonhebel, & Krol, 2010; Xue et al., 2017). Its relevance will continue to

increase in the coming years, which is leading those responsible for the management

processes of this sector to continually seek sustainable growth strategies that allow

competitiveness and long-term survival (Marques, Fuinhas, & Pais, 2018). For

example, it is worth mentioning that around 2050, it is estimated that it will be

necessary to produce and market around 60% more food for some 9 billion people

(FAO, 2019). Food chains have had their growth and international expansion in recent

decades due to the saturation of the national market and the desire to seek more

attractive markets (Aruoma, 2019).

Considering the fast food sector, two aspects must be highlighted: on the one hand, the

strong expansion that the sector has had, being at this moment a very atomized sector

with a strong predominance of some brands over others (Chang & Meyerhoefer, 2019);

and on the other hand, a considerable increase in the consumption of this category of

foods since they have been able to adapt to the needs of the consumer, for example,

introducing healthier products (Lazzarini, Zimmermann, Visschers, & Siegrist, 2016).

In relation to the first aspect, this growth is due: (1) because the brands with the highest

market share have developed strategies taking into account new trends in consumer

habits (Horvat, Granato, Fogliano, & Luning, 2019); and (2) although this dominant

position is a strong barrier to entry, it allows small companies to gain access to a niche

market, thus becoming a highly atomized sector (Kotabe & Kothari, 2016).

Globally, fast food generates revenue of over $570 billion usd - that is bigger than the

economic value of most countries. According to a report from Zion Market Research

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Doctoral Thesis

Héctor Hugo Pérez Villarreal

the global fast food market is expected to be worth more than $690 billion usd in 2022

with a compound annual growth rate of 4.2% from 2017 to 2022 (Zion Market

Research, 2019). The market was capitalized at over $539 billion usd in 2016 (Zion

Market Research, 2019). In addition to growth in sales from drive-thrus, the adoption

of Western fast food in emerging economies is expected to help grow this market

further (Zion Market Research, 2019). Also more hectic lifestyles among dual-income

households and an increased preference for cheap food with no waiting time, in

addition, is expected to positively impact fast food growth, but a shift in preference for

natural and healthy food due to the rising occurrence of obesity in developed countries

could negatively impact the growth of fast food, according to the report (Zion Market

Research, 2019).

However, the sector has been able to adapt to these changes. This evolution reflects an

industry that has responded to changing consumer tastes. Numerous fast food

restaurants are paying attention to the study, evaluation and implementation of

marketing strategies to obtain maximum market share from customers and improve

customer retention to increase the financial performance of the organization (Meghisan

& Meghisan, 2012). This fact has been accentuated by (1) new trends of healthy

consumption, respectful with the environment (Lazzarini et al., 2016); and (2) studies

that show that fast food is not healthy food, causing obesity, heart attacks, etc. (Hobbs

et al., 2019).

Finally, the consumption of food, especially fast food, is characterized by stimulating

values, emotions and attitudes that lead to the construction of the intention to buy a

product (De Wijk et al., 2019; Giraldo, Buodo, & Sarlo, 2019; Gutjar et al., 2015;

Tamuliene, 2015). But at the same time it is one of the sectors that consider the

consumer as a challenge, since it is difficult to know its decision process; for the reason

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of belonging to a sector of massive consumption and low participation (Hsieh &

Chang, 2004; Rivaroli, Baldi, & Spadoni, 2020), without taking into account the

variables that give origin to the purchase intention (Yadav & Pathak, 2016). Therefore,

before consuming a particular fast food brand, the consumer already has the desire to

get it (e.g. McDonald's, Burgen King, KFC, Subway) (Pleshko, 2009; Terblanche &

Boshoff, 2010).

1.2 Justification

Over time, consumer behaviour has undergone significant changes, approaches and

research interests in the field of marketing, since the consumer belongs to one of the

indispensable elements for the existence of marketing (Kumar, 2015). Considering that

the marketing science is constantly evolving, it is of strategic importance to explore

feasible changes and trends that may occur in the future. Technology-enabled market

research involves relevant quantitative methods that enable the retrieval of consistent

sequential information from massive datasets quickly and accurately (Carolan, 2018;

Wang, Bradlow, & George, 2014). In this sense, it is also relevant to investigate the

triggers that create advances in the evolution of this discipline (Kumar, 2015). Within

marketing research, the key idea is to investigate the differences between topics, a

topic that has been of interest to the most prestigious marketing journals of the last

decade (Polonsky, Kay, & Ringer, 2013).

On the other hand, the growing interest in exploring consumer behaviour in relation to

food decision-making processes is also a relevant phenomenon (Rana & Paul, 2017).

Today, it cannot be denied that food industry companies recognize that understanding

the food values that influence consumer decision-making processes is key to success

in competitive food markets (Enneking, Neumann, & Henneberg, 2007; Estiri, 5

Doctoral Thesis


Hasangholi, Yazdani, Nejad, & Rayej, 2010). Indeed, as suggested by Grunert &

Grunert (2006), among the competencies that can increase the level of market

orientation by food channel members is their search for competitive advantages and

the development of consumer understanding. Especially because this understanding is

the key to properly managing relationships with consumers throughout the food

decision-making processes. Therefore, it is not surprising that the companies in this

industry propose to establish strategies to better understand the purchasing behaviour

of consumers (Diaz-Osborn & Osborn, 2016).

In the field of food consumption, one of the trends that draw attention is the

proliferation of consumption of a type of food that has not ceased to gain importance

globally, and that can be considered as the fastest-growing fast food category among

sectors (Goyal & Singh, 2007). This allows us to affirm that the growing consumption

of fast food is an international trend (Belasco, 2014). This growing consumption is

explained by different factors: on the one hand, because competition in this area is

increasing and companies want to have a larger market share and a better positioning

before consumers; but also, on the other hand, by changes in consumer lifestyles

(Belasco, 2014); and demographic growth, in terms of number of people, per capita

income of the city, education and GDP (Beatriz Madeira & Giampaoli, 2017). Then,

since the early 1980s, a large number of publications have been published on the fast

food industry in general (cf. Mcneal, Stem, & Nelson, 1980); more recently a growing

focus on consumption analysis in fast food restaurants (Ghoochani, Torabi, Hojjati,

Ghanian, & Kitterlin, 2018).

Research on consumer behaviour has been very important in order to meet the main

objective of marketing, which is to satisfying consumers needs profitably. This

purpose is affected above all by the importance of decision processes before making

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the purchase or consumption, which is where the need arises. In this case the

interaction of cognitive, affective and conative processes are crucial to establish new

models of decision-making processes based on the changes and evolutions of thinking,

feeling and acting of consumers with respect to food consumption (Gillespie,

Muehling, & Kareklas, 2018; Hwang, Yoon, & Park, 2011).

When consumers make a decision it has always been investigated whether these

decisions were made on the rational side or on the emotional side. Therefore, from this

premise, research towards the knowledge of purchasing decision models has been

fundamental as starting points. Thus, fast food consumers have had to incorporate

rational and emotional variables in the decision-making processes. The interaction of

the right and left brain converges towards the final evaluation of the decision making

of the food consumption (Pentikäinen, Arvola, Karhunen, & Pennanen, 2018).

Therefore, the importance of creating new models of pre-purchase behaviour in fast

food consumption is highlighted.

An essential aspect to highlight is that the attributes of food have become food values.

To initiate this discussion the values of food will be treated according to Lusk (2011).

These are: 1) naturalness, 2) taste, 3) price, 4) safety, 5) convenience, 6) nutrition, 7)

tradition, 8) origin, 9) fair trade, 10) appearance and 11) environmental impact.

The importance of addressing previous studies as primary results of empirical research

will generate a new contribution to the construction of innovative scales based on

hedonic and utilitarian benefits and the effects on the attitudes and purchasing

intentions of fast food consumers (Crowley, Spangenberg, & Hughes, 1992).

On the other hand, the emotions that interact in the purchasing decision process have

been fundamental for the detection of the need (Bagozzi, Dholakia, & Basuroy, 2003).

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Doctoral Thesis


In this step, the emotions emitted before, during and after consumption have been

studied by some theorists (De Wijk et al., 2019). The incorporation of emotions into

consumer pre-purchase models is highly questioned, from the point of view of the

evolution of emotions and the different factors that can modify them. However, the

identification of emotions by consumers has come to prevail as one of the forms of

assessment within needs (Small & Verrochi, 2009). To mention some positive ones,

such as content, surprise, exciting, proud, satisfied, safe, happy, relieved; or negative

ones such as: angry, frustrated, guilty, ashamed, depressed, bored, uncomfortable,

anxious, agitated, nervous, among others.

The theoretical aspect of this research is crucial because it is necessary to investigate

and analyze the different theories, models and theoretical antecedents of consumer

behavior related to food consumption. The research framework will help to raise new

questions and new insights into the fast food industry. Thus, these requirements will

allow companies to understand in greater detail the needs of consumers in order to

provide greater satisfaction. What better research than to start from the origin of the

need to the evaluation processes before obtaining the purchase and consumption of

food.

As a final point, this research is based on different theories about consumer behavior

and emotional marketing. Each stage of the research process will be an essential part

of the integration of the model according to the results of the research. Therefore, this

research has to determine the values of food, benefits, emotions and attitudes according

to the purchase of fast food.

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1.3 Objetives

In consideration of this growing interest, this research will focus on analyzing the

decision process of consuming a specific type of fast food such as hamburgers.

Specifically, this research aims to examine the effect of food values and related

benefits (both hedonic and utilitarian) on attitudes towards hamburger consumption in

fast food restaurants; and to assess the influence of attitudes and benefits (both hedonic

and utilitarian) related to food values on food purchasing intentions. Food choice

decisions are complicated when everyday consumers make many decisions about a

better choice of fast food (Manan, 2016). In recent years, some studies have aimed

primarily to explain how interaction events affect purchase intent through the theory

of planned behaviour (TPB) (Chen & Lu, 2011; Liu, Lin, & Feng, 2018; Yuzhanin &

Fisher, 2016). However, none focused on food values, especially when research

focused on food choice and anticipated positive emotions as a central variable in the

model. Taking into account all these changes, the objective of this Doctoral Thesis is

to analyze the current consumer's behavior with respect to food consumption. In order

to make a broader approach to the object of study, this analysis has been conducted

considering variables of very diverse nature (e.g. variables of values, emotions,

attitudes, purchasing intentions, etc.) in the fast food format. Therefore, this research

is based on the purpose of explaining purchase intent through different additions of

variables in different models. As a result, the objectives are presented:

General objective

- Analyze consumer behavior in the decision process of fast food consumption in

Mexico.

Specific Objectives

9

Doctoral Thesis


- Identify research topics according to the marketing discipline, and determine the

positioning of food-related topics. Specifically, it aims to answer, among others, the

following questions: How is vocabulary commonly used in marketing science? What

are the most relevant issues of these journals? Which articles are the most influential?

Which words do the authors prefer? Is the consumer one of the main topics in

marketing research?

- Analyze consumer behavior in relation to food consumption in fast food restaurants,

paying special attention to pre-decision variables such as: food values, utilitarian

benefits, hedonic benefits, attitude toward eating. In particular, a model is

implemented to explain purchase intention based on the following questions: What is

the effect of food values and benefits (both utilitarian and hedonic) on attitudes toward

eating hamburgers in fast food restaurants? What is the influence of attitudes and

benefits (both utilitarian and hedonic) on intentions to consume this type of food?

- Analyze consumer behavior related to fast food consumption, with emphasis on a

deeper approach from previous research models, such as: food values, anticipated

consumer emotions, attitude toward eating and attitude toward the brand. Adding a

model with greater prediction in the intention to buy from the following prerogatives:

Which variables influence the intention to buy of consumers, taking into consideration

the effect of food values, positive anticipated emotions, attitude toward the brand, and

attitude toward eating hamburgers, on the intention to buy fast food?

On the other hand, the results obtained will help to validate the proposed theoretical

model. The practice of the results will be reflected in the recommendations for the food

sector, as well as for the specific sectors of restaurants and food retailing. It will also

cover different approaches to research and product development, as well as the

10


processes of consumer behaviour in fast food restaurants. The management proposal

to create advantages before producing the product will be addressed in this research.

And finally, key actions or activities will be determined to plan goals in the analysis

of consumer behaviour. The discussions will be motivated to provide brands with fast

food decision making to help them achieve their organizational goals.

1.4 Doctoral Thesis Organization

This research is presented in five chapters. Chapter 1 will contextualize the research

by detailing the introduction, justification, objectives, and work plan. Chapter 2

provides an analysis of the evolution of knowledge and trends in marketing research.

Chapter 3 proposes the first approach to one objective of the study with the exploration

of food values towards the adaptation of utilitarian and hedonic benefits to the

purchase intention. Chapter 4 offers another approach with the inclusion of two

different consumer attitudes through the determination of emotions and food values in

the pre-purchase of fast food. Chapter 5 presents the discussions (See Figure 1.1).

11

Doctoral Thesis


Figure 1.1 Doctoral Thesis Organization


Chapter 2. Identifying reseach topics in marketing science along

the past decade: a content analysis.

Chapter 3. Food values, benefits and their influence on attitudes and intention to buy hamburgers: evidences obtained in Mexico.

Chapter 4. Testing Model of Purchase Intention for Fast Food in Mexico: How do Consumers React

to Food Values, Positive Anticipated Emotions, Attitude toward the Brand, and Attitude toward Eating Hamburgers?

Chapter 5. Discussions

12


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Chang, H.-H., & Meyerhoefer, C. D. (2019). Inter-brand competition in the

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patterns and economic growth. Increasing affluence and the use of natural

resources. Appetite, 55(3), 597–608.

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Kumar, V. (2015). Evolution of Marketing as a Discipline: What Has Happened and

What to Look Out For. Journal of Marketing, 79(1), 1–9.

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21

Chapter 2. Identifying research topics in marketing science along the past decade: a content analysis


Abstract

In recent years, how marketing science is conceptualized has changed, as have the

methods through which data are investigated. This reconceptualization is making a

significant impact on the most important topics of this discipline. Here, a novel

approach is used to analyse a collection of 1,169 abstracts from articles published in

the Journal of Marketing Research and the Journal of Marketing from 2005 to 2014.

It is apply statistical methods to answer the following questions: How is vocabulary

commonly used in marketing science? What are the most relevant topics of these

journals? Which articles are the most influential? What words do authors prefer? Is the

consumer among the primary topics in marketing research? A set of easy-to-read visual

representations are provided to answer these questions. It is highlight two main

findings: (i) consumers and customers are the main topics of these marketing research

journals, which emphasizes the growing interest in consumers and consumer

behaviour as the core of both brick-and-mortar and online businesses; and (ii) in

contrast to previous periods, product has become an essential concept, perhaps due to

the emergence of new product considerations and new and enhanced interrelations.

Keywords: marketing, content analysis, keywords analysis, multivariate statistics.

25


2.1 Introduction

Considering that marketing science is constantly evolving, exploring feasible changes

and trends that might occur in the future is of strategic importance. Technology-

enabled marketing research comprises pertinent quantitative methods that allow for

the retrieval of coherent sequential information from massive datasets in a rapid and

accurate manner (Wang, Bradlow, & George, 2014). In this sense, it is also relevant to

investigate the triggers that create breakthroughs in the evolution of this discipline

(Kumar, 2015). Within marketing research, a key idea is investigating differences

among topics, an idea that has been of interest to the most prestigious marketing

journals in the last decade. These kinds of studies typically use content analysis and

text mining. In a study by Huber, Kamakura, & Mela, (2014) that was published in a

special 50th anniversary issue of the Journal of Marketing Research (JMR), the authors

clustered main topics according to each editor”s tenure. Later, the topics preferred by

each editor were identified by calculating a correspondence analysis (CA). Similarly,

Kolbe & Burnett (1991) reviewed 128 studies that used different kinds of content

analysis as their primary method. Their findings suggested coefficients of reliability

for content-analysis methods. In Morris (1994), the author performed a comparison

between computerized and human outputs, and his results showed that computerized

content-analysis tends to be more reliable and stable.

If these methodologies are applied to conducting a literature review, a common factor

arises: All of these methods are capable of disclosing topics and key concepts on which

researchers are focusing. Additionally, the relevance of these types of studies is

enhanced if they are drawn from the most prestigious marketing science journals,

namely the Journal of Marketing (JM) and the Journal of Marketing Research (JMR).

It is also considering three important academic indexes: Scopus, Thomson-Reuters or

27

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Web of Science (WoS), and ISI. Scopus has more indexed publications than ISI

(Leydesdorff, de Moya-Anegón, & Guerrero-Bote, 2009). However, ISI is considered

more prestigious in the social sciences. According to SCImago (2017), the JM is the

top journal in the marketing industry; the JMR is third. As pioneering publications,

these journals represent the trajectory of the discipline. Currently, they are the official

media of the American Marketing Association (AMA).

In addition to being official media for the AMA, these journals are focused on

demonstrating new techniques for tackling marketing challenges, and thus can be

considered a strong link between theory and practice. According to Thomson-Reuters

indexes, in 2016 the JM had an impact factor of 5.318 and the JMR had a 3.654 impact

factor (Thomson Reuters, 2016). A complementary criterion for evaluating these

journals is their impact factor performance during the last decade (2005-2014). Both

publications should be included on the Journal Citations Reports (JCR) for the

aforementioned period, as shown in the following table.

28


Table 2.1 Impact Factor JCR Marketing Category.

Journal 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Journal of Marketing

4.132 4.831 3.75 3.598 3.779 3.77 5.472 3.368 3.819 3.938

Journal of Supply Chain Management

0 0 0 0 0 5.853 2.65 3.32 3.717 3.857

Journal of Marketing Research

2.611 2.389 1.739 2.574 3.099 2.8 2.517 2.254 2.66 2.256

Marketing Science

3.788 3.977 3.964 3.309 2.194 1.724 2.36 2.201 2.208 1.86

Journal of Consumer Research

2.161 2.043 1.738 1.592 3.021 2.59 3.101 3.542 2.783 3.125

Journal of the Academy of Marketing Science

1.485 1.463 1.18 1.289 1.578 3.269 2.671 2.57 3.41 3.818

Journal of Public Administration Research and Theory

1.451 1.655 1.982 1.509 1.776 2.086 2.176 1.951 2.875 2.833

Academy of Management Perspectives

0 0 0.594 1.118 1.405 2.47 3.75 3.174 2.826 3.354

International Journal of Research in Marketing

1.222 1.28 1.071 1.611 1.873 1.365 1.662 1.781 1.71 1.575

Journal of Retailing

0.894 1.196 2.054 4.095 4.567 2.257 2.75 1.152 1.193 1.754

Source: Own elaboration with 2016 Journal Citation Reports® (Thomson Reuters,

2016)

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Considering the information presented Table 2.1, it is clear that in the marketing area

the JM has been consistently strong over time. On the other hand, although the JMR

did not achieve the highest impact factors for 2013-2014, it earned higher scores from

2007 to 2014. Their respective impact factor scores were considered as a criterion for

selecting these two journals for the present study.

Furthermore, the JM, which has a long tradition in marketing (it is highlight that the

first issue of this journal was published in 1936) and has some of the greatest scientific

relevance, recently published a similar study. In this work, Kumar (2015) discussed

the evolution of marketing science by investigating its “triggers.” The author also

proposes future lines of research and predominant metaphors in the field. Using Kerin

(1996) categorization as a starting point, a new perspective on marketing science is

drawn. By investigating how the topics published in the JMR have evolved as well as

by identifying their corresponding triggers and the scope of the covered topics, the

contributing factors are discussed. A trigger is the influence of academics who

introduce new knowledge in response to practitioners” concerns. These factors

influence the way marketing science will be shaped in the future.

Given this framework for marketing science and bibliometric studies over the last two

decades, the general objective of this paper is to investigate the two most important

journals in the marketing area: the JM and the JMR. This paper is structured in five

sections. First, a literature review, which discusses applications of the techniques

proposed herein, is provided. Then, the methodology is introduced in section three.

Section four contains the obtained results. Discussion and limitations of this research

are presented on the last section.

30


2.2 Literature Review

The historic evolution of marketing science between 1936 and 1945 was accurately

drawn by (Kerin, 1996), who proposed the prominent topic “illuminating marketing

principles and concepts” as a starting point, as well as the metaphor “marketing as

applied economics,” and its triggers “understanding of marketing principles through

case studies,” “need to comprehend government legislation and trade regulations,” and

“marketing research topics and implications for marketing practice.” For the most

recent period, 2013 and onward, the most prominent topic is “marketing at the core

and influence of new media.” Similarly, the related metaphor for this period is

“marketing as an integral part of the organization,” and the triggers are “changes in

media usage patterns,” “focus on marketing efficiency and effectiveness,” and “value

generated by engaging stakeholders of the firm.” Moreover, Huber et al. (2014) study

“A topical history of the JMR” also warrants attention. The way topics and contents

evolved during a 50-year period (1964-2012) is discussed. Huber et al. (2014) also

identify how this journal gradually increased its emphasis on marketing research

methods and advertising, and also expanded its coverage to other substantive topics,

such as consumer behaviour and social networks. Based on this analysis, it can be

inferred that the editorial style of the journal moved from “evolutionary” to

“revolutionary.” The study concluded that during the investigated period, the most

common topic based on the number of published articles was “consumer behaviour.”

Since 1990, the emergence of more powerful computers prompted the proliferation of

two of the most important methods for retrieval data: text mining (TM) and content

analysis (CAN). According to Stavrianou, Andritsos, & Nicoloyannis (2007), TM

focuses on analysing textual data “so that, new previously unknown knowledge is

discovered.” By comparison, CAN attempts to compress large volumes of words and

31

Doctoral Thesis


texts into fewer categories by a given set of coding rules. TM and CAN both aim to

extract common themes and threads by counting words. Although both can use

computer algorithms, TM has the capacity to process natural languages. Meanwhile,

CAN is a systemic and replicable technique, which makes it possible to synthesize a

large number of words into smaller sets of categories (S. Stemler, 2001). For instance,

Stemler, Bebell, & Sonnabend (2011) conducted a content analysis of school mission

statements to identify their primary stated reasons for existence, detect shifts in public

opinion with respect to the passing of time and recognize those schools that introduce

key concepts. Weismayer & Pezenka (2017) investigated keywords in articles

published by International Marketing Review (IMR) from 1988 to 2016 and ENTER

conference proceedings from 2005 to 2016. Their goal was to identify relevant topics

in different research areas and predict trends on published articles. Weismayer &

Pezenka (2017) suggested that CAN is the most valid way to determine editor/reviewer

predilections. Fang, Zhang, & Qiu (2017) conducted a bibliometric study with a five-

step methodology using 105 published articles related to electronic commerce (e-

commerce). The study provided evidence of the suitability of methods such as TM and

CAN for performing literature reviews and bibliometric studies. Nel et al. (2011)

conducted a content analysis of 407 papers published by the Journal of Services

Marketing during 1998-2008 and showed trends in research topics. Similarly, Gläser,

Glänzel, & Scharnhorst (2017) and Muñoz-Leiva, Viedma-del-Jesús, Sánchez-

Fernández, & López-Herrera (2012) found that the number of bibliometric studies,

which apply either TM or CAN, increased.

32


2.3 Methodology

A five-step methodology was implemented to address these research objectives. First,

how data were collected is described, followed by an explanation of the properties of

the dataset. The third step introduces the statistical methods, and details of how the

characteristic words are identified is provided in step four. The software is presented

in the final step (see Figure 2.1).

Figure 2.1 Five-step methodology applied to this research.

33

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2.3.1 Data collection

Over the years, marketing science has changed in terms of its focus, emphasis, and

priorities. In this regard, the JMR and the JM have been forerunners introducing these

changes, thus garnering the attention of academics, businesspeople, and practitioners.

A collection of 1,169 abstracts, which cover the period from 2005 to 2014, were

obtained from the websites of JMR and JM. As additional measures of standardization,

all abstracts included the title, name of the first author, country, university, and year

of publication. Figure 2.2 is a classification of the documents based on country.

Similarly, Figure 2.3 classifies the same group of abstracts according to the year of

publication.

Figure 2.2 Published articles in JMR and JM by country, from 2005 to 2014.

821

76 52 50 22 19 19 16 14 13 11 10 8 7 5 4 3 3 2 2 2 2 2 1 1 1 1 1 1

34


As shown in Figure 2.2, about 70% of the articles published between 2005 and 2014

were submitted by U.S. authors. In second place, the Netherlands accounted for 6.5%

of the publications; Canada was in third place with 4.4% of the articles published.

Researchers from these three countries represent 81% of the all papers published by

both journals. The remaining 19% is distributed among 26 different countries.

Figure 2.3 Published articles in JMR and JM by year, from 2005 to 2014

With respect to the year of publication, the highest number of articles (n = 156) was

published in 2011. In contrast, 2013 was the year with the lowest number, with 99

published articles. In short, between 2005 and 2014, both journals published an

average of 116 articles per year, with a standard deviation of 28.1. In Tables 2.2 and

2.3 information related with published articles by JM and JMR is provided in a more

detailed way.

105 102 108 110134 140

156

12099

143

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

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Table 2.2 JMR Articles by Issue and Year.

Year Issues Editor (tenure)

2005 42 (1): 13 articles, 42 (2): 14 articles,

42( 3): 15 articles, 42 (4): 16 articles

Dick R. Wittink (2003-2005)

Russell S. Winer (2005-2006)


43 (3): 17 articles, 43 (4): 15 articles

Russell S. Winer (2005-2006)

Joel Huber (2006-2009)



Joel Huber

(2006-2009)


45 (3): 10 articles, 45 (4): 9 articles,


Joel Huber

(2006-2009)




Joel Huber (2006-2009)

Tülim Erden (2009-2012)





2011 48 (1): 15 articles, 48 (2): 15 articles, Tülim Erden (2009-2012)

36



48 (5): 10 articles, 48 (Supplement 1): 15

articles

48 (6): 11 articles





Robert Meyer (2012-2016)







51 (5): 7 articles


Source: Own elaboration.

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Table 2.3 JM Articles by Issue and Year.



69 (3): 10 articles, 69 (Special Section): 11

articles,

69 (4): 8 articles

Ruth N. Bolton (2002-2005)

Roland T. Rust (2005-2008)











Ajay K. Kohli (2008-2011)

2009 73 (Special Section): 9 articles,

73 (1): 9 articles 73 (2): 9 articles,

73 (3): 8 articles 73 (4): 8 articles,

73 (5): 8 articles 73 (6): 18 articles





38





75 (5): 8 articles, 75 (Supplement 1): 9 articles,

75 (6): 8 articles





Gary L. Frazier (2011-2014)





2014 78 (1): 8 articles, 78 (2): 8 articles ,


78 (5): 8 articles



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2.3.2 Properties of the dataset

The body under analysis includes 1,169 documents and 120,340 terms. On average,

each abstract contains 103 terms. Regarding the total number of words, the total text

analysed has 185,437 words, which is equal to 158 words for each abstract. This last

measure is relevant because it documents the usual length of abstracts, which is used

by researchers who publish in these journals.

Table 2.4 Descriptive Statistics of the Dataset under Analysis.

Descriptive Statistics Abstract mean Total

Number of terms 103.0 120,340.0

Number of unique terms 71.0 8,874.0

Percent of unique terms 70.4% 7.4%

Number of words 158.6 185,437.0

Average word length 5.9 5.9

Table 2.4 shows the percentage of unique terms. This number refers to words that

appear at least one time in the text regardless of their frequency (a catalogue of words).

The total number of words is obtained by counting all in the document. While the

whole dataset contains 7.4% unique terms, the mean per abstract is 70.4%. The low

percentage of unique terms is a measure of the vocabulary consistency. The percentage

is inversely related to the uniformity of the vocabulary of a given document. With this

40


regard, Bécue-Bertaut (2014) suggested that percentages closer to 1.0 indicate a high

diversity of vocabulary. In this case, it can be inferred that the whole dataset is uniform

in terms of vocabulary use. This makes sense, given that all the abstracts were

published in journals of the same field, and therefore have similar features.

The two most common techniques used for information retrieval are lemmatization

and stemming. Bartol & Stopar (2015) described the first as the methods for removing

inflectional endings on words; Feinerer, Hornik, & Meyer (2008) explained stemming

as the algorithms used for removing word suffixes while preserving their radical. One

advantage of lemmatization is that it first uses glossaries to ensure words are properly

grouped. A limitation observed in this work is that stemming was carried out manually,

and thus is extremely time consuming. Therefore, it is suggesting the use of glossaries

(based on the lemmatization approach) for future research to reduce time spent on

repetitive manual tasks.

Prior to calculating the basic descriptive statistics, the dataset was prepared.

Prepositions, conjunctions, personal pronouns, articles, and demonstratives were

removed. Although the stop-words proposed in the R package “tm” Feinerer (2018)

were used as a reference, the stemming procedures were implemented manually. The

central idea is to reduce text”s complexity without severe loss or distortion of

information. The algorithm proposed by Porter (1980), which has been proven to

provide accurate results for stemming texts in English in a variety of disciplines, was

taken as guideline. Using this approach, corresponding equivalences were obtained;

that is, words with the same meaning and words that appeared in singular and plural

were grouped as one word. For example, the words “accountability,” “accountable,”

and “accounted” should be treated as “account”; the words “branding” and “brands”

should be treated as “brand.” With the purpose of creating graphical representations,

41

Doctoral Thesis


minimum thresholds were imposed. Only words with frequencies equal to 20 and

higher were retained. Similarly, abstracts using a given word 15 times or more, were

also kept. As a result, 994 of the 8,874 different words and 80,123 of the 185,437

occurrences were kept. The yielded document text matrix (dtm) is of order 994 ×1,164.

The rows are related to the abstracts and the columns are related to the words. In

addition, there are three categorical variables in the dataset that relate to year of

publication, author name, and institution. These categorical variables were

incorporated for the last part of the analysis.

2.3.3 Multivariate methods (CA and MFACT)

According to Barahona (2018); Bécue-Bertaut (2014); Benzécri (1980); Murtagh

(2005) CA is widely used in the field of text mining. The most remarkable feature of

CA in the context of a literature review is its capacity for plotting abstracts and words

in such a way that hidden relationships are uncovered. For example, similarities and

differences among abstracts, in terms of the vocabulary used, are identified. Below is

a list of outputs obtained through the CA.

Identifying similarities between abstracts, given their verbal contents.

Detecting similar words, based on their distribution.

Making associations about similar words, given the context in which the words

were used.

Providing visual representations of abstracts and words.

42


Bansard, Kerbaol, & Coatrieux (2006) stated that words frequently used in the same

abstract are all together building a topic and they are considered to belong to the same

metakey. It is important to note that one word can belong to one or more metakeys

(indeed, this is very frequent). This scenario indicates that the same word can be used

in several contexts, each of which might have a different meaning. For instance, the

word “environment” may be related to the quality levels of air or water, but in another

context, “environment” could mean conditions and settings in the workplace. Finally,

CA is capable of quantitatively associating a given metadoc with a metakey that

together characterize the same axis. In this case, it is inferred that abstracts belonging

to a same metadoc are using words, which in turn are associated on the same metakey.

If this lexical table is complemented with the categorical year of publication, then the

analysis changes into a Multiple Factor Analysis of Contingency Tables (MFACT). A

detailed explanation of metakey and metadoc concepts, as well as the results obtained

through both methodologies (CA and MFACT) and their graphical representations, are

provided in section 2.4.2.

2.3.3.1 Types of results

The application of the correspondence analysis and its variants makes the inclusion of

categorical variables possible. This allows to obtain two types of results, as follows:

The first approach comprises results that are commonly obtained through CA:

namely eigenvalues, representations of row-abstracts and column-words, and

distances between abstracts based on both Euclidean and Chi-squared

distances. While the former is given by the squared sum of differences, the last

includes a constant adjustment that is calculated in terms of each column-row

43

Doctoral Thesis


profile. The distributional equivalence, which is a property of a traditional CA,

allows for merging two or more column-profiles that have the same relative

values without affecting distance between row-profiles.

Second, an edited version of the original table is yielded by linking each row-

abstract with the year of publication. The result is a table of quantitative and

categorical variables. The MFACT is a suitable tool for dealing with mixed

data tables (Kostov, Bécue-Bertaut, & François, 2015). MFACT balances the

groups” effect (given by year of publication) on the first dimension by dividing

the columns-words profiles of each group by the first eigenvalue. Then, the

highest inertia of each group is standardized to 1. Interpretation for the MFACT

remains identical to the classical CA. Graphical representations based on the

MFACT allow to compare typologies of each group in a reduced dimensional

space with the purpose of evaluating extent to which positions of row-abstracts

are similar from one group to another.

2.3.4 Characteristic words and abstracts

With the purpose of providing quantitative indicators of the most frequent terms in the

dataset, modelling a hypergeometric distribution (HD) is proposed. HD is a discrete

probability distribution, which defines the probability of achieving k successes in n

attempts, without replacement. Assuming N is a finite population that contains K

successes, the following notation is proposed:

44


– 𝑛.., The total number of words-occurrences in the whole dataset;

– 𝑛, ,, The number of words-occurrences in part j;

– 𝑛 .., The total count of the word i in the whole corpus;

– 𝑛 The count of the word i in part j.

The total frequency 𝑛 of word i in part j is contrasted with other sums. These sums

are obtained with all possible samples composed of 𝑛 occurrences randomly extracted

from the whole dataset without replacement. If word i is relatively more frequent in

part j than in the whole sample, that is: 𝑛 /𝑛 𝑛 /𝑛.. , then the p-value is calculated

as stated in formulas (1) and (2).

1)

j

ij

n

nx

j

j

ii

ji

n

n

xn

nn

x

n

p.

.

..

.....

,

2)

ijn

x

j

j

ii

ji

n

n

xn

nn

x

n

p1

....

,

.

..

.

Based on formulas (1) and (2), a hypothesis test (one-tail) is conducted to assess the

significance of the first eigenvalue, and, consequently, to establish a quantitative link

between chronological evolution and the use of vocabulary. The null hypothesis states:

A chronological dimension of the vocabulary does not exist, and, hence, tested words

are exchangeable across the variable year of publication. Randomly, the variable year

45

Doctoral Thesis


column is permuted in the lexical table without replacement, and a p-value is

calculated on every permutation. An empirical distribution for the first eigenvalue

(under Ho) is obtained by repeating this procedure many times as a number nears 𝑛..,.

The algorithms proposed by Bécue-Bertaut (2014) and Lebart, Salem, & Berry (1998)

are taken as a guideline for these purposes. It is important to conduct a large number

of permutations to compute the p-value as accurately as possible.

2.3.5 Statistical software

The main reasons for using the software R version 3.3.3 (2017-03-06) “Another

Canoe” in this study are detailed below. First, it is open source software, which

allowed to use it at different locations without licence restrictions. Moreover,

considering that R is a collaborative project, Libraries and functions written under the

R environment are constantly up to date, which ensured that state-of-the-art

computational algorithms were used in the analysis. Specifically, the function

BiblioMineR (Hernández Ramírez, 2012) and the packages CA Greenacre, Nenadic,

& Friendly (2017) RcmdrPlugin.temis (Bouchet-Valat & Bastin, 2013), and

FactoMineR (Lê, Josse, & Husson, 2008), among others, were utilized.

2.4 Results

2.4.1 Glossary of most frequent terms

The first analysis of the glossary of most frequent terms allowed to conclude that this

is a repetitive corpus. Note that only 25 words represent 24% of the occurrences in the

46


whole dataset, which is equal to 28,881. “Consumers” was the most frequent word

with 1,527 occurrences, which means that it appears in 47% of the abstracts. “Product”

was second (1,450 occurrences), followed by “customer” (1,269 occurrences),

appearing in 36% and 24% of the abstracts, respectively. These three terms with

“effect” (1,239 occurrences), “brand” (1,160 occurrences), “marketing” (1,077

occurrences), and “firm” (1,019) shape the main content of both journals. The results

show that nearly 28% of the abstracts include all of these words together. This overall

perspective allows taking a first approach in identifying what seems to be of interest

to JMR and JM authors. Their efforts are directed toward discussing effects on

products, consumers, and brands through “study” (880), “model” (748), “market”

(691), “research” (659), and “price” (617).

These findings yield supporting evidence that consumer behaviour was one of the most

relevant topics during the investigated decade. The terms “consumers” and “customer”

are among the top ten recurrences for the whole dataset. Moreover, these results are

similar to those obtained by Huber et al. (2014) which highlighted how the JMR gave

increasing importance to the topic of consumer behaviour during the investigated

period. Moreover, conclusions obtained by Huber et al. (2014) in relation to the term

“product” also drew some attention. Consistent with these results, they ranked

“product” at position nine of prevalence in abstracts for 1964-2012. It appears in

second place of the rankings in the current study (see Table 2.5). Considering this, it

is inferred that the concept of “product” gained more attention in the last decade in

contrast to previous periods (1964-2001).

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Table 2.5 List of the 25 Most Frequent Terms.

Word Glossary

Frequency

No.

Documents

consumer 1527 554

product 1450 417

customer 1269 284

effect 1239 574

brand 1160 228

marketing 1077 442

firm 1019 336

study 880 518

use 786 537

model 748 360

market 691 282

research 659 462

price 617 173

data 577 394

48


relationship 522 222

value 509 203

sale 495 169

decision 456 236

performance 455 193

choice 440 183

level 426 253

show 398 334

behaviour 386 222

find 383 302


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2.4.2 Most relevant topics and its related abstracts

Correspondence analysis is a multivariate statistical technique that is applied to

categorial data and provides means for summarizing large datasets on a reduced

dimensional space. In this context, CA is applied to identify those “metakeys” and

“metadocs,” which better describe similarities among abstracts based on the words

they use. It is important to clarify that a metakey is related to a given word used in one

or more abstracts, whereas a metadoc is related to an abstract. In this way, two or more

metadocs might be related in function to the same metakeys. Researchers might

identify the set of words (metakey+/metakey-) that most contribute to the inertia and

lie on the positive/negative part of the axis. Simultaneously, the set of documents that

most contribute to the inertia (metadoc+/metadoc-) and lie on its positive/negative part

might also be identified.

For the purpose of creating intuitive visualizations, only those metakeys and metadocs

with strong presence on the principal axes were considered. Abstracts using a given

word 15 times or more were kept. Words with frequencies equal to 20 and higher were

also retained. According Lebart et al. (1998), this improves the comprehension of

associations among words and abstracts. The first five components, obtained through

the correspondence analysis, were retained. From this group, the pair with the highest

eigenvalues was taken as axes of the charts provided below. While the eigenvalue for

the first axis is equal to 0.25, its value for the second axis is 0.21. These two axes are

able to accurately describe the emergence of the most relevant words of the

investigated dataset, taking into account that they also have the biggest eigenvalues.

Note that previously mentioned rules apply only to visual representations (Figure 2.4).

Additional criterion, which consisted of retaining only those words and abstracts with

50


a contribution three times higher than the mean (average), was applied for elements

listed in Table 2.6.

Figure 2.4 Most contributory abstracts / words (CA).

With regard to Figure 2.4, note the positive part of the first axis, which is also called

(DIM1+), which notes a set of words (metakey+) that is closely related with a

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Doctoral Thesis


metadoc+. The words “consumer,” “choice,” “price,” “consumption,” “preference,”

and “self” are highlighted in this area. It is also identifying the negative section of the

first axis (D1M1-), where the metakey- is located (consumer relationship). It is

composed of the words “customer,” “firm,” “marketing,” “relationship,”

“performance,” “business,” and “market.” At the same time, the mentioned words are

closely related to the metadoc-, which is composed of the articles “363,” “715,” “731,”

and others.

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Table 2.6 Main Topics.

DIM TOPICS METAKEYS

DIM

1+ Consumer Choice

“consumer” “choice” “price” “consumption” “preference” “self”

“people” “option” “attribute” “food” “product” “hedonic” “brand”

“evaluation” “goal” “extension” “experiment” “search” “health”

“purchase” “less” “versus”

DIM

1-

Customer Relationship

Management

“customer” “firm” “marketing” “relationship” “performance”

“business” “market” “satisfaction” “supplier” “value” “orientation”

“employee” “return” “service” “stock” “capability” “financial”

“shareholder” “management” “innovation” “ties” “organizational”

“portfolio” “relational” “risk” “equity” “trust” “metric” “governance”

“frontline” “salesperson” “development” “knowledge” “manager”

“loyalty” “network” “strategic” “retention”

DIM

2+

Developing strategies

and programs for

pricing

“price” “retailer” “store” “model” “search” “pricing” “manufacturer”

“demand” “data” “household” “advertising” “endogeneity”

“category” “elasticity” “distribution” “retail” “method” “promotion”

“elasticities” “channel” “private” “parameter” “heterogeneity”

“estimates” “grocery” “market” “channels” “share” “unobserved”

“sale” “estimation” “shopping” “estimate” “label” “competitive”

“quantity” “optimal” “competition” “profit”

DIM

2- Emotional Marketing

“self” “emotion” “employee” “emotional” “evaluation” “goal”

“message” “regulatory” “brand” “extension” “hedonic” “people”

“fit” “experience” “corporate” “influence” “personality”

“knowledge” “frontline” “utilitarian” “identity” “process” “positive”

“participation” “attitude” “study” “consumption” “engagement”

“role” “processing” “versus” “focus” “service” “negative”

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DIM

3+

Design and

management of

integrated marketing

channels

“supplier” “price” “goal” “customer” “service” “relationship”

“consumption” “pricing” “saving” “food” “trade” “decision”

“employee” “people” “business” “buyer” “aversion” “option”

“salesperson” “choice” “ties” “seller” “outcome” “reference”

“retailer” “performance” “orientation” “hedonic” “manufacturer”

DIM

3- Brand Equity

“brand” “extension” “personality” “association” “advertising”

“branding” “equity” “fit” “branded” “category” “success” “stock”

“value” “metric” “return” “similarity” “risk” “measure” “image”

“shareholder” “measures” “attitude”

DIM

4+

Design and

management of

integrated marketing

communications

“marketing” “advertising” “method” “media” “choice” “design”

“model” “stock” “search” “review” “conjoint” “attribute”

“recommendations” “approach” “traditional” “complexity”

“network” “investor” “rating” “web” “advertisement” “site”

“emotion” “option” “heterogeneity” “metric” “firm” “respondents”

“content” “response” “activity” “preference” “decision” “researcher”

“social”

DIM

4- Marketing Channels

“price” “brand” “extension” “retailer” “store” “manufacturer”

“private” “supplier” “label” “image” “category” “retail” “pricing”

“employee” “loyalty” “labels” “shopping” “promotion” “reference”

“grocery” “share” “personality” “national” “success” “frontline”

“service” “identification” “discounts” “business” “evaluation”

“buying”

DIM

5+ Value Networks

“supplier” “extension” “governance” “method” “trust” “model”

“relationship” “conjoint” “knowledge” “attribute” “partner” “design”

“ties” “choice” “network” “measurement” “parameter” “approach”

“brand” “performance” “selection” “proposed” “relational”

54


“decision” “innovation” “organizational” “predictive” “unobserved”

“incentive” “preference” “approaches” “validity” “distribution”

DIM

5- Marketing Metrics

“stock” “advertising” “return” “emotion” “risk” “investor” “price”

“spending” “shareholder” “satisfaction” “negative” “finance”

“financial” “message” “impact” “review” “systematic” “food” “loss”

“firm” “long” “promotion” “equity” “store” “term” “health” “value”

“consumption” “cash” “positive” “abnormal” “metric” “short”

“search” “emotional” “online” “expenditures” “net” “customer”


Similarly, metakey2+ is distinguished by the topic “Developing strategies and

programs for pricing.” Note that it is located in the positive part of the second axis

(D1M2+), and it is composed of the words “price,” “retailer,” “store,” “model,”

“search,” and “pricing.” Note that articles “92,” “77,” and “277” compose metadoc2+.

With respect to the negative part of (D1M2-), “emotional marketing” is identified as

the most remarkable topic. The articles that feature this topic are “118,” “309,” “1045,”

and “726.”

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Table 2.7 Distribution of Abstracts/Words.

Aggregation of abstracts and words according to the categorical variable year

Years Abstracts

Occurrences

before

Occurrences

after

Mean

length

Words

before

Words

after

2005 105 12251 6122 116.68 2507 881

2006 102 13285 6675 130.25 2653 912

2007 108 15577 7903 144.23 2849 929

2008 110 16497 8402 149.97 2912 939

2009 134 19516 9827 145.64 3207 957

2010 140 19286 9415 138.75 3321 965

2011 156 21729 10558 139.29 3466 958

2012 120 17381 8855 144.84 3069 947

2013 51 6734 3304 132.04 1818 752

2014 143 19377 9596 135.50 3269 935

Overall 1169 161633 80657 138.27 8800 994


56


While the most contributing metakeys (words) are related to a given topic and also

introduced in Table 2.6, the way abstracts and words were aggregated in respect to the

year of publication is presented in Table 2.7. The criterion for selecting words and

abstracts was their contribution to the total inertia. In Table 2.6, those contributions

higher than three times the mean (average) of the total inertia were kept. In Table 2.7,

elements equal or higher than the mean of the total inertia are presented. In both cases,

axes with the biggest eigenvalues are used as references.

2.4.3 Chronological evolution

To investigate the chronological evolution of the vocabulary, the abstract-words

matrix was transformed into a mixed table by adding the variable year of publication

as a categorical variable. Consequently, the CA turned out to be a MFACT. This makes

it possible to identify similarities and differences in vocabulary over time. Periods

characterized by specific terms and important variations in the use of the vocabulary

were also identified. By conducting this analysis, it can answer questions such as:

Which groups of documents, given a year of publication, are similar or different?

Which periods are characterized by the introduction of new vocabulary? How has

vocabulary evolved over time?

The input for the MFACT consisted of a mixed table on which the categorical variable

year of publication is distributed on rows. Columns are reserved for words. In this

form, the matrix contains 10 rows (years) and 994 columns (words). The eigenvalues

for the first five components (obtained from the MFACT) are presented in Table 2.8.

Note that the eigenvalues are, in general, smaller than those obtained through the

traditional correspondence analysis. Typical structures on mixed tables are among the

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Doctoral Thesis


main causes of the small eigenvalues. These properties were exhaustively studied by

(Greenacre et al. (2017); Kostov et al. (2015); Lebart et al. (1998) among others. While

the eigenvalue for the first component is 0.032, the value for the second is 0.026. The

same rules previously applied to the CA are repeated for the MFACT: retain five axes

in the initial calculation and select the two with the biggest eigenvalues. Finally, the

projection of words with a contribution of three times higher than the mean (average)

was carried out. Therefore, it ensured that the most representative words were

visualized, either due to the biggest eigenvalues on the axes or the high contribution

of the chosen words.

Table 2.8 Eigenvalues for First Five Components

Measures Dim.1 Dim.2 Dim.3 Dim.4 Dim.5

Eigenvalues 0.032 0.026 0.023 0.020 0.020

% Variance 18.23 14.34 12.70 11.46 11.00

Cumulative 18.23 32.57 45.27 56.72 67.72

2.4.4 How has the vocabulary evolved over time?

A type of big picture of how the vocabulary had evolved over the years is shown in

Figure 2.5. There are three important periods where the vocabulary shifted: 2005-2006

in blue, 2007-2009 in grey, and 2010-2014 in green. For the horizontal axis, while

words related to “customer satisfaction” and “market model” are displayed on the

negative part of the axis, words referring to “social networks” and “mobile

58


technologies” are projected in the positive area. With respect to the vertical axis, the

positive area is characterized by the words “regulatory,” “fit,” and “retailer.” On the

negative part, the words “brand,” “networks,” “demonstrate,” and “stock” are found.

In the first period, from 2005-2006, authors published in the journals were mainly

writing about regulatory issues, emotional shopping, and fitting models. During the

second period (2007-2009), authors focused their attention on the customer”s

satisfaction, loyalty, and trust. Topics such as market models, branding, firm returns,

and stocks are characteristic of this period. Finally, in the third period, which

comprises 2010-2014, topics such as social networks and contents, mobile

technologies, online shoppers, reviews, and demonstrations emerged.

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Doctoral Thesis


Figure 2.5 Visual representation of years and words (MFACT).

This draws the attention to the radical change in words that emerged during period

three, in contrast to the previous periods. It is clear that authors focused their attention

on contemporary issues, including the proliferation of online marketing and social

networks. Table 2.9 presents the characteristic words according to each analysed

period. In the first period (2005-2007), the results identified words such as “fit,”

“manufacturer,” “regulatory,” and “model” among others. The second period is

characterized by the words “firm,” “stock,” “loyalty,” “efficiency,” “competitive,”

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

-0.2

0.0

0.2

Dim 1 (18.23%)

Dim

2 (

14

.34

%)

2005

2006

2007

2008

2009

2010

2011

20122013

2014

emotionalfit

manufacturers

models regulatory

retailershopping

competitive

customer

firm

market

model

satisfaction

trust

brand

loyalty returnsrisk stock

content

food

group

media

reveal

reviews

self

social

spendingdemonstrate

website

mobileratings

online

shoppers

networks

60


“risk,” and others. Finally, the third period gathered words such as “media,” “social,”

“network,” “customer,” and “mobile.” These results are consistent with the work

proposed by Karvanen, Rantanen, & Luoma (2014), who observed the growing

relevance of social media in contemporary marketing research.

Table 2.9 Characteristic Words by Period

Period Characteristic words

2005-

2006

fit, manufacturer, regulatory, model, aversion, net, web, retailer, relationship,

satisfaction, article, price, intention, author, emotional, structural, enhanced,

shopping, involvement, relational, bias, parameter, reference, retailing

2007-

2009

firm, stock, loyalty, efficiency, competitive, risk, finance, customer, promotion,

investments, market, chain, corporate, trust, manager, duration, valuation,

revenue, shares, benefits, improvement, industry, impact, equity, scholars,

interface, competitors, costs, marketing

2010-

2014

media, social, consumer, group, spending, rating, reveal, demonstrate, user, line,

review, product, sale, food, content, online, advertising, network, employee, goal,

position, campaign

Finally, in Figure 2.6, the periods are shown again. Rather than highlight just those

words that characterize each period, each main topic is included in this visualization.

For instance, topics including regulatory issues, emotional aspects, and fit models

shape the first period. The second period features topics of consumer satisfaction and

trust, firm risk, and stock returns. The most recent period is made up of topics such as

social media, food reviews, food studies, online consumers, and mobile technologies.

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In this form, the investigated period was accurately clustered into smaller ones by

considering content similarities of each abstract included in the analysis.

Figure 2.6 Periods of evolution for the vocabulary in the first MFACT plane.

62


2.5 Discussion and Limitations

In this research, a collection of 1,169 abstracts from over the course of a decade was

investigated by proposing novel forms of applying classical statistical methods. All

abstracts correspond to articles that the most prestigious journals in the field have

published (JM and JMR). First, basic descriptive statistics of average words per

abstract, the percentage of unique terms, and average word length were provided.

Thereafter, the most frequent words were identified and allowed to disclose the

authors” preferred vocabulary. By conducting a correspondence analysis, the most

influential abstracts were identified. Finally, a multifactor analysis of contingency

tables was calculated to disclose how the use of vocabulary has evolved. Three

important periods that characterize how vocabulary has evolved over time were

disclosed.

This analysis gives evidence to the importance that authors have put on customer

issues. That is, the consumer was the center of marketing research during the

investigated decade. Similarly, the term “product” comes next in importance. This is

obvious, considering that marketing practices are almost meaningless without at least

one product. The word “client” does not appear in the top rank, but its presence

increases in the third and last period, which is unsurprising, as client and consumer are

the same in most cases. The word “effect” also warrants attention because marketing

science is having an effect on organizations and people. Finally, the word “brand”

draws the attention because it is one of the foundations of contemporary marketing.

This case study provides value for academics, researchers, and practitioners within the

marketing science area by tracking and identifying the most relevant publications with

respect to periods of time and topics. By providing easy-to-read visualizations, readers

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Doctoral Thesis


can promptly identify those articles that made significant contributions in the field or

locate specific publication niches. This work also illustrates how literature reviews in

marketing can be effectively conducted while also reducing time spent. The main

topic, “customer choice,” plays a strategic role in establishing a link between the

consumer and purchasing decisions. Two additional primary topics of interest are

“developing strategies” and “programs of pricing.” This lends supporting evidence to

the idea that pricing policies are relevant to contemporary marketing, considering that

pricing policies encompasses concepts as “action indicators,” “performance

measures,” and “profitability metrics.” These results provide partial support for the

popularity that Customer Relationship Management (CRM) has gained in recent years.

In this respect, topics most related with CRM are “added value,” “orientation,” and

“service.” Here, the importance of having long-term relations with customers, which

is a core concept in marketing science, is also highlighted. “Emotional marketing” is

another main topic that recognizes the generation of knowledge in this discipline by

investigating individuals emotions.

This work also contributes to the discussion of how literature reviews can be

performed, within marketing science or in other disciplines. The primary goal was to

propose useful methods for classifying publications according to content similarities.

The methods presented here might be used as general guidelines for authors and

researchers who are interested in performing literature reviews in a systematic way.

By identifying the specialized vocabulary that is used in this discipline and later

incorporating it into their documents, authors may be assured that they are at the

forefront of modern vocabulary usage.

Text mining is an emerging discipline. As such, there are still some significant

limitations. Taking into account that only 1,169 abstracts were incorporated in this

64


study, the results are more illustrative than truly generalizable. Therefore, the results

are not providing compelling evidence about one accurate “radiography” of marketing

science; this work is much more modest. Rather, the main objective was to

demonstrate the suitability of text mining techniques for conducting precise and

standardized literature reviews. A broader investigation should include the full text of

each article to improve the accuracy of these results. Moreover, categorical variables

such as research center, country, and keywords should be incorporated to better

describe the ideal profile of the authors. A second paper, which effectively

incorporates these ideas, is currently in progress.

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Doctoral Thesis


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Chapter 3. Food values, benefits and their influence on attitudes and intention to buy hamburgers: Evidence

obtained in Mexico

Chapter 3. Food values, benefits and their influence on attitudes and intention to buy hamburgers: Evidence obtained in Mexico

Abstract

Food values have been proposed as determinants of purchase intention in fast-food

restaurants. The objective of this research is twofold: (1) to analyze the effect of food

values and their related benefits (both hedonic and utilitarian) on attitudes toward

eating hamburgers in fast-food restaurants; and (2) to evaluate the influence of

attitudes, food values, and their related benefits (both hedonic and utilitarian) on the

intention to consume this kind of food. To do this, this research adapted the food values

scale proposed by Lusk & Briggeman (2009) to the context of fast-food restaurants.

The data were collected from a survey of 512 Mexican fast-food consumers and

analyzed using SEM. The results show that people’s attitudes toward eating

hamburgers and the food’s hedonic benefits, exert a strong influence on the intention

to buy; in contrast, food values and utilitarian benefits have a relatively lower influence

on people’s attitudes toward eating hamburgers.

Keywords: Food values, utilitarian benefits, hedonic benefits, attitudes toward eating

hamburgers, purchase intention.

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3.1 Introduction

In recent years, both academics and managers have taken a special interest in exploring

consumer behavior in terms of the food decision-making process. This interest

encompasses a few different phenomena: From an academic perspective, some studies

(e.g., Barahona, Hernández, Pérez-Villarreal, & Martínez-Ruíz, 2018) have found how

the term food have been acquiring a growing relevance in the marketing discipline,

especially in relation to particular fields such as consumer choice and design and

management of marketing channels. The literature is also devoting greater attention to

relatively new concepts such as food values by analyzing their role in food purchasing

and consumption processes (Lusk, 2011; Lusk & Briggeman, 2009; Martínez-Ruiz &

Gómez-Cantó, 2016). These efforts speak to a broader attempt at understanding and

forging bonds with consumers, as reflected in marketing approaches such as Marketing

3.0 and Marketing 4.0 (Martínez-Ruiz & Gómez-Cantó, 2016).

Meanwhile, from a managerial perspective, companies in the food industry recognize

that success in today’s competitive food markets begins with understanding how

product attributes influence consumers’ food decision-making processes (Enneking,

Neumann, & Henneberg, 2007; Estiri, Hasangholi, Yazdani, Nejad, & Rayej, 2010).

As (Grunert & Grunert, 2006) suggests, developing an understanding of consumers—

and particularly how to manage relationships with them—can be a key competitive

advantage for food companies. Unsurprisingly, then, many companies are trying to

devise strategies to better understand consumers’ purchasing behaviors.

In this regard, it is important to consider that consumers’ food choices are more

complex than ever before, which has made it all the more difficult to understand and

predict such behavior (Grunert & Grunert, 2006). Many studies have tried to identify

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Doctoral Thesis


consumer preferences for specific product attributes, without taking into account the

wide range of products at consumers’ disposal that are characterized by an even wider

variety of attributes (Lister, Tonsor, Brix, Schroeder, & Yang, 2017). For this reason,

Lusk & Briggeman (2009) studied the general classifications of food values, which

express more abstract attributes that can explain consumer purchases over time. These

food values often encompass numerous physical attributes simultaneously and may be

responsible for consumers preferring one product over another (Lusk, 2011; Lusk &

Briggeman, 2009).

However, a product’s associated food values can differ from the benefits it confers to

consumers. These benefits can substantially influence subsequent marketing outcomes

such as satisfaction, repeat purchases, recommendations, etc. (Bloch, 1986; C. Otnes,

A. Ruth, & Marie Crosby, 2014). In order to satisfy their needs, consumers emphasize

values that provide certain benefits related to pleasure, utility, or in some cases, both

(Ghosh Chowdhury, Murshed, & Khare, 2018). The relevant literature has

traditionally considered benefits in terms of a product’s attributes: For example,

Chitturi, Raghunathan, & Mahajan (2008) categorized said benefits as hedonic and

utilitarian, while Crowley, Spangenberg, & Hughes (1992) noted that these benefits

can be high or low depending on the product. Consistent with work done by Chitturi

et al. (2008), this research distinguishes between the hedonic and utilitarian benefits

associated with food values.

Another variable to consider in the food decision-making process is consumers’

attitude. In general, people’s attitude toward consuming a product stems from their

perceptions of the object’s attributes or characteristics (Mowen & Minor, 1998;

Verbeke & Viaene, 1999). Because attitudes influence behavior, they can help to

explain consumers’ food choices. Furthermore, attitude influences consumers’

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intention, which is an intermediate step between attitude and behavior. Intentions

reflect a person's decision to perform a certain behavior, which will only be taken when

the person has total control over the behavior (Fishbein & Ajzen, 1975).

In the field of food consumption, scholars have devoted substantial attention to fast

food, which remains the fastest-emerging food category all over the world (Goyal &

Singh, 2007). Consequently, fast food consumption has become an international trend

(Lang, 2003) that can be explained by different factors: On one hand, there is

increasing competition among companies for a larger market share and better customer

positioning; on the other hand, there has been momentous demographic growth (in

terms of the number of people, income per capita, education and GDP; Beatriz Madeira

& Giampaoli, 2017) alongside changes in consumer lifestyles (Lang, 2003).

Unsurprisingly, there has been a huge number of publications on the fast-food industry

since the 1980s (Mcneal, Stem, & Nelson, 1980), with recent researchers devoting

their attention to analyzing consumption at fast-food restaurants (Ghoochani, Torabi,

Hojjati, Ghanian, & Kitterlin, 2018).

Building on this growing interest, the present work analyzes the decision process

behind consuming a particular type of fast food: hamburgers. Specifically, this study

aims to examine the effect of food values and their related benefits (both hedonic and

utilitarian) on people’s attitudes toward eating hamburgers in fast-food restaurants, as

well as to evaluate the influence of attitudes, food values, and their benefits (both

hedonic and utilitarian) on the intention to acquire such food. The remainder of this

paper covers the following: In the next section, it is present the conceptual framework

and the hypothesized relationships between variables. Afterward, it is describe the

empirical work and the subsequent results. Finally, the paper concludes by discussing

the main findings and managerial implications.

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3.2 Conceptual Framework

In the field of food, one of researchers’ major tasks has been to explain food

consumption behavior (Tuu, Olsen, Thao, & Anh, 2008). Among the diverse theories

advanced for such purposes Shepherd (1989), Ajzen, (1991) theory of planned

behavior (herafter, TPB) has attracted broad application and empirical support in

several domains, such as the intention to eat pizza, snacks, genetically modified food,

meat, beer, a low-fat diet or healthy foods (e.g., Louis, Davies, Smith, & Terry (2007);

Tuu et al., (2008). This theory originates within the expectancy–value tradition of

attitude–behavior research, and offers a simple model with a big virtue of parsimony

(Eagly & Chaiken, 1993). In recent years, scholars have suggested several extensions

and modifications to this theory to improve its predictive and exploratory power

(Armitage & Conner, 2001; Conner & Armitage, 1998; Tuu et al., 2008). Against this

background, it can see this adopt this theoretical framework to assess the intention to

consume a certain kind of food (i.e., hamburgers). Importantly, it will utilize attitude

toward consuming this food, as well as food values and their related benefits, as

predictors of people’s intentions.

3.2.1 Food values and benefits

Traditionally, the scientific community has shown great interest in the relationship

between individuals and their food purchase decisions. Today’s consumers face a

complex environment for food choices; thus, understanding and predicting the choice

process has become a difficult task. Some have even claimed that consumers act

irrationally or even randomly when choosing food products (Grunert & Grunert,

2006). However, it may be more accurate to say that consumers have developed more

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dynamic, complex and differentiated demands, and thus their food choices are

influenced by multiple aspects (Grunert & Grunert, 2006).

However, consumers assign different importance to the attributes of a given food

product. Many studies have tried to identify consumer preferences for specific product

attributes, but this task is confounded by the fact that consumers have a wide range of

products at their disposal, which feature an even wider variety of attributes or

characteristics (Lister et al., 2017). For this reason, Lusk & Briggeman (2009) studied

the general classifications of food in the form of food values, which express more

abstract attributes that can explain consumer purchases over time. In this sense,

consumers base their product choices on a set of inferred food values, which often

encompass numerous physical attributes simultaneously (Lusk & Briggeman, 2009).

Specifically, Lusk & Briggeman (2009) identified the food values of naturalness, taste,

price, safety, convenience, nutrition, origin, fairness, tradition, appearance, and

environmental impact. Lusk (2011) adopted these same values, but later researchers

added others such as animal welfare or novelty (see for instance, Bazzani, Gustavsen,

Nayga, & Rickertsen, 2018). Likewise, some research (e.g., (Izquierdo-Yusta, Gómez-

Cantó, Pelegrin-Borondo, & Martínez-Ruiz, 2019) has focused on segmenting

consumers according to food values. In this vein, these authors identified three groups

of consumers: 1) mainly utilitarian, focusing on food values such as price; 2) mainly

hedonic, focusing on food values such as taste; and 3) mainly ethical, focusing on

values such as environmental impact. Put differently, consumers pursue food products

on the basis of some objective(s), which are reflected in the benefits that consumers

perceive in the product.

Along this line, Batra & Ahtola (1991) suggested that consumers buy goods and

services and perform consumption behaviors for two basic reasons: (1) consummatory

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affective (hedonic) gratification (from sensory attributes), and (2) instrumental,

utilitarian reasons related to achieving some result. Hedonic benefits are oriented

around increasing the likelihood of a pleasant experience and, by extension, positive

emotions, while utilitarian benefits are oriented around balancing functional objectives

with the related sacrifices (e.g., of time, money) (Batra & Ahtola, 1991; Dhar &

Wertenbroch, 2000; Voss, Spangenberg, & Grohmann, 2003). Therefore, hedonic

benefits are more subjective and personal; utilitarian benefits are more geared toward

achieving a task (Babin, Darden, & Griffin, 1994). Because people generally prioritize

the avoidance of harm or pain and treat pleasure as a luxury, utilitarian benefits are

often emphasized over hedonic ones.

Building on this argument, Chitturi et al. (2008) proposed that hedonic and utilitarian

consumption fundamentally differ in the emotional experience they offer: delight or

satisfaction, respectively. Therefore, the authors predicted that the type of positive

emotional response evoked by consuming a product depends on whether the offer

exceeds the expectations for utilitarian or hedonic benefits. They argued that exceeding

utilitarian expectations will only result in satisfaction, but exceeding hedonic

expectations will produce a feeling of delight. On the contrary, failing to fulfill

utilitarian or hedonic expectations would cause anger or dissatisfaction, respectively.

It is important to note that, in recent years, health-related food attributes have become

just as important to consumption as non-health related attributes such as taste, sensory

appeal, familiarity, or convenience. In particular, Maehle, Iversen, Hem, & Otnes

(2015) pointed out how taste was higher for hedonic products than for utilitarian

products; and price was lower for hedonic products than for utilitarian products due to

the differences in price sensitivity for utilitarian and hedonic products (Wakefield &

Inman, 2003). Moreover, environmental friendliness was found to be lower for a

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hedonic product than for a utilitarian product, which contradicts the investigation of

Lascu (1991). The importance of the healthiness was lower for utilitarian food

products than for the hedonic ones. In addition, Raghunathan, Naylor, & Hoyer (2006)

argued that unhealthy products are preferred when a hedonic goal is more salient. For

Cramer & Antonides (2011), Loebnitz & Grunert (2018), and (Khongrangjem et al.,

2018), taste was considered a hedonic factor.

From these ideas, and consistent with Batra & Ahtola, (1991); Chitturi et al. (2008);

Dhar & Wertenbroch (2000); Strahilevitz & Myers (1998), it will use utilitarian

benefits to refer to the functional, instrumental and practical benefits of food values,

and hedonic benefits to refer to the aesthetic, experiential, and enjoyment-related

benefits of food values.

3.2.2. Attitudes and intention

In the field of cognitive psychology, attitude is the main factor that guides and

determines human behavior (Bredahl, 2001). Appropriately, attitude is an important

predictor of the intention to consume food (Bonne, Vermeir, & Verbeke, 2008; Saba

& Di Natale, 1998; Tuu et al., 2008).

In this regard, the Theory of Reasoned Action (hereafter, TRA) usefully encapsulates

the attitude-behavior relationships that link attitudes with subjective norms, behavioral

intentions and behavior in a fixed causal sequence. This theory presumes that behavior

is a direct function of intention, which is itself a function of attitude and subjective

norm. Moreover, a person’s attitude towards performing the behavior is deemed to be

a summed product of individuals' beliefs and their evaluation of said beliefs (Ajzen &

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Fishbein, 1980). This theory underlies Ajzen (1991) Theory of Planned Behavior

(TPB), in which the attitude toward the behavior reflects the degree to which a person

has a favorable or unfavorable appraisal of the behavior in question. In this setup,

people’s beliefs about the outcome of the behavior, as well as their evaluations of these

outcomes, produce an ‘attitude towards the behaviour’ (Ajzen, 1991). TPB posits that

people will be more likely to engage in a given behavior when they hold a positive

attitude toward participating in said behavior.

In general, people’s attitudes toward an object (in this case, a food product) result from

a perceived combination of the object’s attributes or characteristics (Mowen & Minor,

1998; Verbeke & Viaene, 1999). When consumers hold a positive attitude towards a

certain food product, they will be more likely to purchase said product and probably

show a positive attitude toward the providing establishment. In this vein, Haws &

Winterich (2013) described four key aspects of people’s attitude toward eating

hamburgers: pleasure, enjoyment, satisfaction, and good taste.

Any discussion of attitude should account for intention, which serves as a bridge

concept between attitude and behavior. Previous studies have identified a positive

predictive relationship between people’s attitude toward and intention to buy and

consume a food product (Haws & Winterich, 2013; Zhang et al., 2018). For example,

Thøgersen (2009) and Chen (2009) suggested that a positive attitude galvanizes

consumers’ intention to purchase organic food. Likewise, Chen (2009) found that

people’s attitudes toward eating hamburgers influences their purchase intention. In

general, in the context of fast food, it has been commonly found how the attitude

toward eating hamburgers is a relevant variable in behavioral intention toward eating

with an emphasis in healthy consumption, with a dependence on fast food representing

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a strong barrier to healthier food (Chan & Tsang, 2011; Close, Lytle, Chen, & Viera,

2018; Luomala et al., 2015).

3.2.3. Hypotheses

Following the framework of TPB (Ajzen, 1991), this research aimed to assess people’s

intention to consume a certain kind of food (hamburgers) while incorporating several

variables as predictors (i.e., food values, their related benefits, and people’s attitudes).

Building on the idea that consumption decisions can be complex, this study accounts

for the formation of attitudes and preferences that underlie behaviors (Jun, Kang, &

Arendt, 2014). Consequently, this study incorporated aspects related to certain food

values—such as nutritional value, taste value, price value, or the benefits associated

with these types of values—that are likely to influence attitudes. For example, some

research (Ghoochani et al., 2018; Law, Hui, & Zhao, 2004) has highlighted that the

importance consumers place on health can influence their attitudes toward food.

Moreover, Mattsson & Helmersson (2007) concluded that Swedish high school

students are generally aware of fast food’s negative side effects and accordingly pay

more attention to nutritional and health concerns than to price, speed, and convenience.

On the other hand, Jekanowski et al. (2001) found that fast-food demand depends on

its availability. Given the above, this study developed the following hypotheses:

H1. Food values are positively and significantly associated with attitudes

toward eating hamburgers.

H2. Utilitarian benefits related to food values are positively and significantly

related with attitudes towards eating hamburgers.

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H3. Hedonic benefits related to food values are positively and significantly

related with attitudes toward eating hamburgers.

Scholars have found that people’s intention to acquire fast food largely depends on

their attitudes and the benefits associated with the products. For example, preserving

health and well-being is a cornerstone concern for many consumers; thus, they want

to understand the nutritional value of what they eat and strive to follow a balanced diet

that decreases the risk of obesity and chronic diseases. Given this concern, many

studies have tried to verify the importance of such variables on people’s intention to

eat fast food. For instance, Dunn, Mohr, Wilson, & Wittert (2008) found that

consumers are largely aware of the high fat content of fast foods, and yet generally

appreciate their taste and convenience. Thus, they may experience an ambivalence

toward fast food that reflects a trade-off in decision-making: between short-term

rewards (as captured by affective responses toward taste and convenience) and long-

term costs (as reflected in understanding the cumulative health risk). How people

resolve this ambivalence likely depends on the consideration they give to future

consequences when making decisions (Dunn, Mohr, Wilson, & Wittert, 2011).

Strathman, Gleicher, Boninger, & Edwards (1994) argued that people who consider

the future consequences of their behaviors are more likely to forgo immediate reward,

whereas their counterparts tend to have trouble delaying gratification and display little

concern for the longer-term effects of their behaviors (Dunn et al., 2011). This is a

particularly important issue for health-promoting behaviors, such as diet and exercise,

which tend to produce negative outcomes in the short-term. Scholars have argued that

an ability to foresee and value the future consequences of health-related behaviors

likely plays a part in the formation of the related intention (Sirois, 2004). For many,

eating fast food has a positive short-term consequence in terms of immediate satiation

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and hedonic pleasure (Dunn et al., 2008), even though the long-term consequences of

regularly eating energy-dense food are generally assumed to be negative. Based on the

above, and applying the TPB framework, it could be advance the following

hypotheses:

H4. Utilitarian benefits related to food values are positively and significantly

related with purchase intention.

H5. Hedonic benefits related to food values are positively and significantly

related with purchase intention.

H6. Attitude toward eating hamburgers is positively and significantly related

to purchase intention.

In sum, this research tested six hypotheses inspired by the literature (illustrated in

Figure 3.1).

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Figure 3.1 Model development.

3.3 Methodology

To test the model proposed in Figure 3.1, this study designed a questionnaire intended

to obtain information related to participants’ socio-demographic profile and the study

variables (food values, utilitarian benefits related to food values, hedonic benefits

related to food values, attitudes toward eating hamburgers, and purchase intention).

For the food-value variables, we adapted the scales from Lusk & Briggeman (2009)

and Lusk (2011). These questions focused on the importance that respondents assigned

to these corresponding values on a scale from 1 (least important) to 5 (most important).

In contrast, to assess the hedonic and utilitarian benefits, the food values scale was

adapted to the hedonic and utilitarian benefits, using a 5-point Likert scale (where 1

was the least important and 5 the most). Finally, the attitudes and purchase intentions

variables were obtained from the literature review and adapted to this research, using

a 5-point Likert scale (where 1 was the least important and 5 the most). This research

86


distributed the survey among consumers in Puebla City, Mexico. Participation was

voluntary, and in the end, 512 participants completed the questionnaire.

Table 3.1 Technical details of the research

Universe Residents in Metropolitan Area of Puebla-

Tlaxcala, Mexico

Sample unit People over 17 years old and buyers of fast food

Data collection method Personal survey

Sample error P=q=0.5; 5% K= 2; e = ±4.335

Level of reliability 95%

Sample procedure Probabilistic

Number surveyed 512 valid surveys

Period of information collection January 26 - May 23 (2018)

Fast food restaurant McDonald’s

3.4 Analysis

The participants were 58% female and 42% male. Almost 80% were between 17-34

years old, and about 70% were single. Moreover, 62.9% had bachelor’s degrees, while

32.8% had a monthly income below 300 USD (See Table 3.2).

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Table 3.2 Sample characteristics.

Variable Items (%) Variable Items (%)

Gender Male 42 Marital

status

Single 69.9

Female 58 Married without children 6.4

Age 17-24 34 Married with children under 15 12.1

25-34 44.5 Married with children over 16 6.8

35-44 10.9 Divorced 0.8

45-54 5.7 Divorced with children under 15 1.4

55-64 3.9 Divorced with children over 16 1.6

65-74 0.6 Widowed 1

75-84 0.4 Income Less than 300 USD 32.8

Study

levels

Less than high school 5.1 301-450 USD 16.2

High school 16.6 451-600 USD 19.4

Bachelor 62.9 601-750 USD 12.1

Graduate / Master 15.4 more than 751 USD 19.5

The PLS SEM was used (in conjunction with the SmartPLS 3.2.8 software) to validate

the model proposed in Figure 3.1. To establish the significance of the parameters, this

method performed bootstrapping with 10,000 resamples. To ensure construct

reliability and validity, it was examined the indicator loadings for the reflective

constructs. Those items with a loading of less than 0.7 were omitted (J. Hair,

Hollingsworth, Randolph, & Chong, 2017). The ‘food values’ variable was considered

a formative construct. Unlike reflective indicators, formative indicators are not

interchangeable; therefore, omitting a single indicator can reduce the validity of the

measurement model’s content (Diamantopoulos & Papadopoulos, 2010).

88


In the next step, it was calculated the construct reliability and validity. The most

commonly used criterion is that proposed by Jöreskog (1971), which establishes that

values over the 0.7 to 0.9 range are considered good or very good (see Figure 3.2).

Also it was calculated Cronbach’s alpha, composite reliability, and average variance

extracted (AVE). The Cronbach’s alpha coefficient was acceptable, as all constructs

achieved a coefficient greater than .7 (J. F. Hair, 2010). Similarly, the AVE of each

individual construct exceeded the acceptability value .5 (Fornell & Larcker, 1981;

Huang, Wang, Wu, & Wang, 2013). In fact, the composite reliability (CR) values

below .6 indicate a lack of internal consistency reliability (J. Hair et al., 2017). In the

same way, the Rho A is considered homogenous if this index is larger than .7 (Werts,

Linn, & Jöreskog, 1974) (see Table 3.3).

Table 3.3 Construct reliability and validity

Cronbach’s alpha rho A (CR) AVE

Attitudes toward eating hamburgers .847 .858 .898 .687

Food values N.A. (1) 1.0 N.A. N.A.

Hedonic benefits related to food values .831 .850 .887 .662

Purchase intention .862 .901 .916 .784

Utilitarian benefits related to food values .852 .857 .887 .529

N.A. (1) = Not Applicable

Afterward this study was examined discriminant validity, which is apparent if the correlation coefficient of two dimensions is less than the square root of the AVE (Fornell & Larcker, 1981).

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Table 3.4 Discriminant validity

Attitude toward eating

hamburger

Food

values

Hedonic

benefits

Purchase

intention

Utilitarian

benefits

Attitude toward eating

hamburger .829

Food values .437

Hedonic benefits related

food values .07 .652 .814

Purchase intention .519 .390 .448 .885

Utilitarian benefits

related food values .459 .540 .726 .463 .727

After evaluating all the measurement instruments’ psychometric properties, the model proposed was estimated in Figure 3.1. The estimated final model is shown in Figure 3.2 and Table 3.5.

Table 3.5 Path coefficients

Hypothesis Relationship Beta t-value p-value

H1 Food values -> Attitudes .166 3.235 .001***

H2 Utilitarian benefits -> Attitudes .170 3.156 .002***

H3 Hedonic benefits -> Attitudes .275 4.652 .000***

H4 Utilitarian benefits -> Purchase intention .200 3.400 .001***

H5 Hedonic benefits -> Purchase intention 0.131 2.095 0.036**

H6 Attitudes -> Purchase intention .380 8.162 .000***

R2 Attitude = .290; R2 Intention = .359

Note: *** p < 0.001, ** p < 0.05

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Figure 3.2 Structural model

Regarding the validity of all constructs, Figure 3.2 illustrates the factor loadings of

indicators on the assigned construct; therefore, they have to be higher than all loading

of other constructs with condition that the cut-off value of factor loading is higher than

.7 (Fornell & Larcker, 1981). Note that the PLS algorithm produces loadings (weights)

between reflective (formative) constructs and their indicators.

For the formative construct (food values), the best-rated items were taste, tradition,

appearance and convenience (.524; .325; .275; .234). However, the same construct

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showed negative weights for environmental impact, nutrition and origin (-.238, -.232,

-.037). For the utilitarian benefits related to food values, the best loadings were safety

and convenience (.776; .774), while price did not form a strong part of the construct.

For the hedonic benefits related to food values, the best loadings were taste and

convenience (.853; .827). Both constructs share some items with levels of utilitarian

and hedonic composition.

Finally, it was calculated the mediating effect of attitudes in relation to utilitarian /

hedonic benefits and intentions. Following several steps in order to test the indirect

effects in PLS (adapted from Chin, 2010). Specifically, these are the steps developed

in the works of Zhao, Lynch, & Chen (2010) and Nitzl, Roldan, & Cepeda (2016). The

first step involves evaluating the significance of the indirect effects (AxB). To test this

significance, bootstrapping with 10,000 subsamples has been performed and the values

of the direct effects obtained have been multiplied. For the second step, it was

determined the type and magnitude of the indirect effect. To this end, it was calculated

the Variance Accounted For (VAF), which assesses the size of the indirect effect on

the total effect (direct effect + indirect effect) (Hair, 2014). In other words, this test

determines the extent to which the mediation process explains the variance of the

dependent variable (Carrión, Nitzl, & Roldán, 2017).

In the case of the present investigation, it has been observed that the mediating effect

of the attitude and the relationship between Utilitarian Benefits and Purchase

Intentions does not occur, as can be seen in the following formula:

(0,170*0,380) / ((0,200+(0,170*0,380)) = 24.4% Partial Mediation

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Regarding the mediating effect of the attitude and the relationship between hedonic

benefits and purchase intentions, it does not occur, as can be seen in the following

formula:

(0,275 * 0,380) / ((0,131 + (0,275 * 0,380)) = 44.37 % Partial Mediation

With all the information it was confirmed support for all the hypotheses through the

path coefficient, standard error, t-value, and p-value. The most important effects (from

greater to least effect) were: attitude on purchase intention (H6), hedonic benefits on

attitudes (H3); utilitarian benefits on purchase intentions (H4); utilitarian benefits on

attitudes (H2); food values on attitudes (H1) and hedonic benefits on purchase

intention (H5).

As can be seen in Table 3.5, Hypothesis H6, Attitudes > Purchase Intentions, is the

relationship that obtained the greatest β, .380 (ρ = .000 ***). This finding aligns with

previous research about the importance of attitudes on intentions. The relationship with

the second-greatest weight was the one postulated by H3 (β = .275; ρ = .000 ***),

underscoring the importance of the hedonic benefits provided by food values on the

formation of attitudes. The third-most important relationship was the one posited by

H4 (β = .200; ρ = .001 ***), which highlights the importance of utilitarian benefits on

intentions. This finding suggests that consumers apply rationality to the choice

process, as they assign more weight to utilitarian benefits than hedonic benefits on the

intention to buy. The fourth-most important relationship, captured by H2 (β = .170; ρ

= .002 ***), underlines the importance of utilitarian benefits on attitude. Meanwhile,

the support for H1 (β = .166; ρ = .001 ***) suggests that food values influence

attitudes; even though β did not reach a very high value in the model, this relationship

is still meaningful. Since these are the values that form consumer attitudes towards this

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type of food, it should be noted that given the type of food, the fact that consumers

show favorable attitudes is important. Finally, the relationship posited by H5 (β = .131;

ρ = .036 **), regarding the influence of the hedonic benefits on purchase intentions,

the contrast of the hypotheses corroborates the rationality of the consumer. It should

be noted here that although the hedonic benefits have a very strong weight on the

attitudes, these - hedonic benefits - are not as important as in the moment prior to the

purchase decision, where it was verified how the utilitarian benefits are more relevant.

In sum, the results substantiate the following points: (1) that attitudes influence the

intentions toward future behavior, in line with the TPB; (2) that when forming attitudes

toward consuming hamburgers in fast-food establishments, consumers consider food

values that align with Lusk and Briggeman’s scale (2009); (3) that consumers base

their initial attitudes towards consuming fast food more on the hedonic component (the

affective dimension of attitude), but when considering whether to buy the product

again, they apply more value to the utilitarian component (the behavioral dimension

of attitude).

3.5 Conclusions

Given the massive growth and success of the fast food industry, scholars have been

eager to analyze the strategies of the most important brands and translate those

strategies to other sectors. This aligns with a desire among many companies to not

only seek a larger market share and better consumer positioning, but also to adapt to

changing consumer lifestyles and demographic patterns. On this basis, the present

work analyzed consumers’ decision-making process with regard to a specific type of

fast food (namely, hamburgers). To this end, it was showed the effect of food values

and their related benefits (both hedonic and utilitarian) on people’s attitudes toward

94


eating hamburgers in fast-food restaurants, as well as the influence of attitudes and

food value-related benefits (both hedonic and utilitarian) on the intention to acquire

such food.

It is found support for all proposed hypotheses. First, with regard to the influence of

food values on attitude, this research revealed the importance that consumers attach to

each value on the proposed scale. Specifically, respondents assigned the most

importance to taste, tradition, appearance and convenience. Previous studies have also

emphasized these values and as in this research, the price is not one of the values that

have much weight. The latter is in line with the product category, since its cost is

reduced. It is noteworthy that the weights for the values “environmental impact”,

"nutrition" and "origin" (although to a very minor extent) are negative. The low weight

for “environmental impact” could suggest that consumers think that eating hamburgers

has little impact on the environment. Similarly, the results for “nutrition” and “origin”

could indicate, respectively, that consumers are aware that hamburgers are “unhealthy”

and do not particularly care about the origin of this type of food.

Second, these results align with prior research in stressing the importance of hedonic

and utilitarian benefits. However, it should be noted that "convenience" has been

valued as a hedonic benefit. Sometimes, the “delight” of a meal, or dinner, can be

frustrated by the waiting times to be attended, to the delay in providing the service

(once requested). In other words, it appears that a benefit with a utilitarian character

(in this case, convenience) can assume a hedonic shape.

As for the utilitarian benefits, all of them have been very well valued by the consumers.

Highlighting here as a benefit, which can be considered hedonic-appearance-has been

valued as a utilitarian benefit. This result seems reasonable: When assessing a product,

95

Doctoral Thesis


consumers consider its costs in the context of its presentation, which can then come to

seem like a utilitarian benefit.

In addition, both hedonic and utilitarian benefits positively support the formation of

positive attitudes towards hamburger consumption, at least for those sold by

McDonald’s. It is noteworthy of this last aspect, since it is not the same to eat a product

such as hamburgers when the whole process is controlled by the consumer, or when

attending a restaurant (not fast-food), which has some quality indicator (Michelin stars,

number of forks, etc.), when consumed at McDonald's, where no quality process is

controlled and is considered a fast-food restaurant.

In addition, this study highlights the mediating effect of attitude on the relation

between utilitarian / hedonic benefits and intentions. This finding confirms that both

benefit types exert weight on intentions, but this weight is nonetheless higher for

hedonic benefits. This is a novel finding, as prior research has not tested these

mediating effects.

Finally, it was observed an association between attitudes toward eating hamburgers

and purchase intention, which is consistent with previous research.

In terms of managerial implications, fast-food companies should keep in mind that

consumers have settled on the idea that their hamburgers are “unhealthy” (negative

weight of food values), but still like this food type for values such as taste, tradition,

appearance and convenience. In the long term, these companies should adjust their

advertising messages to emphasize healthier values. New consumer segments are more

informed, look at nutritional information, value eating “healthy” products that do not

harm the environment, and may be willing to pay a premium price to satisfy their

preferences. In this regard, it is important to remember that people’s consumption

attitudes can change in tandem with the shifting importance assigned to hedonic and

utilitarian benefits.

96


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Table 3.6 Variables and measure

Latent

variable

Observed variables How to measure

Food

values =

general

food

attributes

consumers

believed

were

relatively

more

important

when

purchasing

food.

Appearance = extent to which food looks appealing Source: Lusk (2011); Likert

scale 1 - 5 (1 = not at all

important to 5 = extremely

important)

Convenience = ease with which food is cooked

and/or consumed

Environmental = effect of food production on the

environment

Fairness = the extent to which all parties involved in

the production of the food equally benefit

Naturalness = extent to which food is produced

without modern technologies

Nutrition = amount and type of fat, protein,

vitamins, etc.

Origin = where the agricultural commodities were

grown

Price = the price that is paid for the food

Safety = extent to which consumption of food will

not cause illness

Taste = extent to which consumption of the food is

appealing to the senses

Tradition = preserving traditional consumption

patterns

Utilitarian

benefits

related to

food values

UB appearance = the appearance and presentation of

the product is useful and necessary

Adapted from Lusk (2011);

Likert scale 1 - 5 (1 = not at all


important). The items were

constructed according to the

UB convenience = the convenience of consumption

and preparation is useful for me need to eat

UB fairness = by consuming favoring fair trade is

useful and necessary

110


UB nutrition = the nutrition obtained from eating a

hamburger is useful to what I need at a certain time

adaptation of food values with

the utility of the product.

UB origin = the origin of the hamburger I think is

elementary when consuming the product

UB price = the price of the hamburger is adequate

for the need to eat that I have

UB safety = the safety of the food helps me to satisfy

my need to eat

Hedonic

benefits

related to

food values

HB appearance = the appearance and presentation of

the product give me pleasure

Adapted from Lusk (2011);

Likert scale 1 - 5 (1 = not at all


important). The items were

constructed according to the

adaptation of food values with

the pleasure of the product.

HB convenience = the comfort of consumption and

preparation of the hamburger is pleasant

HB safety = the safety of the hamburger gives me

pleasure

HB taste = the taste of the hamburger gives me

pleasure

Attitudes

toward

eating

hamburger

s

ATE1 = Eating a hamburger would be pleasurable Adapted from Haws and

Winterich (2013); Likert scale

1 - 5 (1 = strongly disagree to

5 = strongly agree)

ATE2 = I would enjoy eating a hamburger

ATE3 = Eating a hamburger would be satisfying for

me

ATE4 = I eat hamburgers because of the good taste

they have

Purchase

intention

PI1 = You probably buy McDonald’s products Adapted from Chiu, Hsieh and

Kuo (2012); Diallo (2012);

Likert scale 1 - 5 (1 = strongly

disagree to 5 = strongly agree).

PI2 = I would consider buying McDonald’s products

if I need a product of this type

PI3 = It is possible to buy a McDonald’s product

PI5 = The probability that you would consider

buying a McDonald’s product is high

111

Chapter 4. Testing Model of Purchase Intention for Fast

Food in Mexico: How do consumers react to food

values, positive anticipated emotions, attitude toward the

brand, and attitude toward eating hamburgers?

Chapter 4. Testing Model of Purchase Intention for Fast Food in Mexico: How do consumers react to food values, positive anticipated emotions, attitude toward the brand, and attitude toward eating hamburgers?

Abstract:

This research investigated the effect of the food values, positive anticipated emotions,

attitude toward the brand, and attitude toward eating a hamburger on purchase

intention in fast food restaurants in Mexico conjointly. The purpose of this study was

to discover which variables influenced the consumer´s intention to buy. Data was

collected from a survey of 512 Mexicans fast-food consumers. Structural equation

modeling was used to test the hypothesized associations. The results showed that food

values and positive anticipated emotions absolutely impact the attitude toward the

brand, which impacts the purchase intention of the Mexican consumers. Nonetheless,

the positive anticipated emotions impact stronger than food values and the best way to

get a purchase intention is toward the attitude of the brand rather than attitude toward

eating a hamburger. The authors discussed inferences and suggestions for consumer

approaches.

Keywords: food values; positive anticipated emotions; attitude toward the brand;

attitude toward eating a hamburger; purchase intention

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4.1 Introduction

Food choice decisions are complicated when every day the consumers make a lot of

decisions about one excellent fast food (Manan, 2016). Over the past few years, some

studies have a primordial objective to explain how interaction facts affect purchase

intention through theory planned behavior (TPB) (Chen & Lu, 2011; Liu, Lin, & Feng,

2018; Yuzhanin & Fisher, 2016). But none focused on the food values, especially when

the research was about food choice and positive anticipated emotions like a central

variable in the model. Based on a dataset of 1,169 abstracts of marketing from 2005 to

2014, Barahona, Hernández, Pérez-Villarreal, & Martínez-Ruíz (2018) explained that

one crucial dimension for researchers is emotional marketing. Topics such as

evaluation, experience, message, people, emotional, goal and hedonic are the

keywords for studies in this field. Therefore, this research was based on the purpose

of explaining the purchase intention in four main premises. First, the fast food

consumption has a purchase intention by the attitude toward the brand into the means

of an emotional need according to a physiological desire (Ding & Tseng, 2015;

Handley, 2010; Ruth, 2001). Second, the consumers´ emotions influence the purchase

intention (Wang, 2009). Third, what is the role of food values to attitude toward the

brand and attitude toward eating a hamburger (Goldsmith, Freiden, & Henderson,

1995)? Fourth, what is more essential to predict the purchase intention: attitude toward

the brand or attitude toward eating a hamburger (Lorenz, Langen, Hartmann, & Klink-

Lehmann, 2018)?

Through this research, a model with these variables was proposed because there is a

synergistic effect between them. The approach rests with the effects of food values and

positive early emotions directed towards the form of the attitude as a predecessor of

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Doctoral Thesis


the purchase intention (Koenig-Lewis & Palmer, 2014; Song & Qu, 2019; Zhao, Deng,

& Zhou, 2017). This model was designed from the separation of attitudes: one directed

towards the act of eating and another towards the brand. The application covers the

principle on attitudes directed towards the product and another towards the brand.

Thus, this model is the first that uses the rational and emotional part of consumption

and separates the attitude of eating from the attitude towards the brand. In this case,

the model provides information on the importance of the product and the brand and

towards launch, modifications and valuations of products and brands. The consumer’s

decisions are based on some level of rational or emotional effect (Nicolini, Cassia, &

Bellotto, 2017; H. Zhang, Sun, Liu, & G. Knight, 2014).

This study forms the rational (food values) and emotional (positive anticipated

emotions) parts to connect them with different attitudes to predict purchase intention.

Consequently, it used these two attitudes roles, eating versus brand, to test the

relationship to purchase intention. The importance of the study is to predict the

purchase intention and to knowing the consumers’ behavior choices with a hamburger.

If the calculations, weights, loadings, etc. contribute to explaining more of the

purchase intention, it should make an important and significant contribution to

academic literature. This is because it gives off too many forms to investigates and

implement strategies in fast food restaurants, knowing the protrusion factors in the

model.

For these reasons, it is intended to identify which emotions, food values and types of

attitudes impact significantly and positively on the purchase intention. Through these

findings, marketing strategies can be formulated and it is possible to know what the

most convenient way for this field is. The objective of the present study was to

explicitly test the purchase intention toward attitudes, food values and positive

118


anticipated emotions. The study built a model on purchase intention research by

examining the consumer before the purchase decision. Also, this study emphasized the

meaning of the role of attitudes (eating hamburger and brand) on purchase intentions

of fast food consumers. Finally, the study tested and confirmed the hypotheses planted

in this research.

4.1.1 Attitudes in consumer behavior

Attitude toward something is an antecedent of intention, but it is also the degree to

which an individual has a favorable or unfavorable evaluation or appraisal of the

behavior to any purchase situations (Ajzen, 1991). Some research has also highlighted

the role of purchase intention and the attitude impact (Ajzen & Fishbein, 1980). On

the other hand, the attitude that is formed in the first stage is formed of the decision

process of purchase in the consumer (recognition of the need/problem). Some studies

proved that the attitude directly affects the consumer's buying behavior (Garg,

Wansink, & Inman, 2007; Talih Akkaya, Akyol, & Gölbaşı Şimşek, 2018; Wu, 2003).

This attitude is influenced by elements such as information, nature of the product,

social media, ads and other behavioral factors. In the context of food consumption, the

role of attitudes is at the top for research in consumer behavior. Thus, some consumers

have attitudes toward eating hamburgers and others have attitudes toward the brand.

This is because they keep both positive and negative evaluations, such as purchases

intentions, purchases and repurchases (Chang, 2011). However, in marketing as a

discipline, the gap is different between attitude toward eating hamburger and attitude

toward the brand.

Attitudes toward eating hamburgers play a significant role in understanding consumer

behavior. These attitudes can be decision-making components for the choice and

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Doctoral Thesis


intention to eat some food (Chen, 2009; Ghoochani, Torabi, Hojjati, Ghanian, &

Kitterlin, 2018). Once consumers recognize their need for food, they enter into a stage

of searching and evaluating the alternatives (Bai, Wang, Yang, & Gong, 2019). It is at

this stage, where people positively or negatively value the desired behavior without

implying the degree of eating habits or the level of hunger (Coricelli, Foroni, Osimo,

& Rumiati, 2019). Hence the attitude of eating evaluates the favorable or unfavorable

predisposition towards the act of eating any food (Ajzen, 1991). Rezai et al. (2017)

(Rezai, Teng, Shamsudin, Mohamed, & Stanton, 2017) pointed to a direct relationship

between attitudes towards eating foods that generate a healthy benefit and the intention

to buy. For this reason, it is vital to know one’s attitude towards the act of eating as a

central point towards the intention to buy.

On the other side, attitudes are cognitions and can sometimes be directed towards the

brand (Diallo & Seck, 2018). So it is necessary to comment that attitudes towards the

brand can generate a behavioral intent and the same behavior of the consumer's final

purchase (Johye Hwang, Yoon, & Park, 2011). Therefore, attitudes towards the brand

mean that consumers adopt or reject conduct based on experiences, personal

recommendations and media exposure, as well as other media that use the brand and

may have a point of contact with the consumer (Foroudi, 2019). Hence, attitudes

towards the brand have become one of the intangible components valued by consumers

because when choosing the behavior, they do it more for the brand than for the product.

Similarly, the attitude towards the brand makes consumers acquire feelings of security,

confidence, convenience and credibility among others, so for them, it is easier to

recognize and choose the purchase (Jeng, 2016). Thus, the literature agrees that

attitude towards the brand is the highest point through which the consumer

disseminates the choice.

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4.1.2 Purchase intention

Assael (1992) called purchase intention the conduct that seeks in response to an object

and is before the purchase. Subsequently, Zhang et al. (2018) approved the relationship

between attitudes and purchase intention. Phau & Teah (2009) demonstrated when the

consumer has a strong positive attitude; more is the intention to buy.

Rezai et al. (2017) pointed out the importance of determining the intention to purchase

functional products from examining the factors involved in the purchase decision

process. For example, Jahn, Tsalis, & Lähteenmäki (2019) indicated that the general

attitude towards products has a direct effect towards the intention to purchase, as long

as the people are in a condition of suitability and knowledge of the problem. Asif,

Xuhui, Nasiri, & Ayyub (2018) pointed out that it is possible to find differences in

intent to buy from one country to another, but they agreed that attitude and health

awareness are the best predictors of the intention to buy in organic foods. Some studies

pointed to some additional variables to the TPB including moral attitude and healthy

awareness towards purchasing intent in organic foods (Yadav & Pathak, 2016).

Consequently, it is possible to include other variables in the purchase intention by

extending the TPB. On the other hand, another study pointed to the involvement

towards the consumption of products, price sensitivity and moderation of the effect of

the identity of the local product towards the intention of purchase (Ghali-Zinoubi &

Toukabri, 2019).

Chiu, Hsieh, & Kuo (2012) and Diallo (2012) underlined aspects about the probability

to buy, not before the consumer formed an attitude and experience of the past. Now,

as the intention is testified to be a significant factor of buy, it was thus, hypothesized

that:

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H1. Attitude toward the brand will positively influence intention to buy.

H2. Attitude toward eating hamburger will positively influence the intention to

buy.

4.1.3 Food values

The situation of obtaining information on the attributes of the product has always been

a relevant topic in food consumer research. Today, exotic consumption attributes,

towards the ethics of consumption, healthy awareness, animal impact and organic food

are topics of interest in knowing one’s behavior (Clarkson, Mirosa, & Birch, 2018;

Ditlevsen, Sandøe, & Lassen, 2019; Ghvanidze, Velikova, Dodd, & Oldewage-

Theron, 2017; Raaijmakers, Sijtsema, Labrie, & Snoek, 2018). According to Basha &

Lal (2019), the ratio of environmental concern, health, and lifestyle, supporting local

farmers, product quality, convenience, price, animal welfare, safety-trust, subjective

norms and attitude is valued. The food choice has been becoming an advantage to

improve healthy and sustainable diets and to know the different roles of high and low

involvement Boer & Schösler (2016). Nevertheless, Boer & Schösler (2016)

mentioned the differences in the affinities could be predicted by food-related value

motivation.

Sprotles & Kendall (1986), through consumer styles inventory (CSI), claimed that

consumers choose to make their purchase decision through eight basic styles: high

quality, innovation, brand awareness, price, hedonism, confusion with other brands,

impulsivity and habit. Other studies emphasized product presentation, food safety,

environmental impact and ethical consumer identity (Jiyoung Hwang, 2016). Another

study found that depending on the type of food (organic or conventional) used, the

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effect on the consumer perception component (e.g., healthy consciousness) differs

Rana & Paul (2017).

When researches talk about the food attributes, it can be partial with the real concept

because the food attributes can be an infinite number of characteristics, but only some

of them are important for the moment of choice (Martínez-Ruiz & Gómez-Cantó,

2016). For this reason, the attributes of the product became the consumer's values

regarding food. Some researchers affirmed that these values were influenced through

many factors, which relate to personal values (Lang & Lemmerer, 2019; P. Y. Lee,

Lusk, Mirosa, & Oey, 2014; Manan, 2016; Tey et al., 2018). This means that food

values are exercised by the consumer and not by the product itself. However, each

attribute mentioned above falls within a factor of the eleven described by Lusk (2011).

Thus, it is possible that each product, depending on belonging in the category,

constitutes intra-group differences, but it is possible to categorize them in general

forms.

Lusk & Briggeman (2009) explored all the factors that integrated the attributes of food.

After this plan, Lusk (2011) opened wide eleven items to identify the food values scale.

These items are 1) naturalness (the extent to which food is produced without modern

technologies), 2) taste (the extent to which consumption of food is appealing to the

senses), 3) price (the amount paid for food), 4) safety (the extent to which consumption

of food will not cause illness), 5) convenience (the ease with which food is cooked and

consumed), 6) nutrition (the amount and type of fat, protein, vitamins, etc.), 7) tradition

(preserving traditional consumption patterns), 8) origin (where the agricultural

commodities were grown), 9) fairness (the extent to which all parties involved in food

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Doctoral Thesis


production equally benefit), 10) appearance (the extent to which food looks appealing)

and 11) environmental impact (the effect of food production on the environment).

Studies have shown food values are essential to explain the attitudes. For example,

Manan (2016) emphasized the attitudes toward personal values, but the question is

whether personal values are influenced the food benefits, then these affect attitude. In

order, Lang and Lang & Lemmerer (2019) demonstrated the relationships across

personal values and attitudes toward local food, but they did not separate the attitude

toward eating a hamburger or the attitude toward the brand. As a result, it is

hypothesized that:

H3. Food values will positively influence attitude toward the brand.

H4. Food values will positively influence attitude toward eating a hamburger.

4.1.4 Anticipated emotions

Some researchers have been in charge of framing emotions as a fundamental, principal

axis and detonator of all purchasing behavior, this adding the part of information

processing and consumer action (Agrawal & Duhachek, 2010; Berger & Milkman,

2012; Hsee, Yang, Zheng, & Wang, 2015; Levav & Mcgraw, 2009; Poor, Duhachek,

& Krishnan, 2013; Teixeira, Wedel, & Pieters, 2012; Wood & Moreau, 2006).

Although the entire chain of observation (cognitive, conative and affective), the trigger

and the key factors of success cannot be established, some researchers have taken a

part of the chain towards the effective and successful verification of the application of

branding emotional, buyback, purchase decision, search and evaluation of purchase

alternatives (Golder, Mitra, & Moorman, 2012; C. J. Lee & Andrade, 2011;

Strahilevitz, Odean, & Barber, 2011; Thompson, Rindfleisch, & Arsel, 2006).

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Within the contributions of advertising, it is possible to highlight that the emotional

contagion may have main effects on the physiological changes of the people (Small &

Verrochi, 2009). In this study, the participants felt sadder when they saw a victim with

a sad face, and their sadness emanated the effect on the expression of the emotion in

the sympathy. The effects of contagion are automatic and not inferential but are

diminished by deliberative thinking. On the other hand, Nielsen, Shapiro, & Mason

(2010) showed that the "pre-attention" processing of semantic information in non-focal

announcement titles can provoke orientations towards attention responses. The same

results in foreseeable increases in the ad and knowledge of the brand. Equally, Teixeira

et al. (2012) showed that surprise and joy concentrate effective attention and retains

the viewers with more time. But, the most important thing is the level of retention

instead of the speed of surprise, and it affects more the concentration of attention.

Therefore, speed influences the level of joy, which affects spectator retention. These

three studies placed the emotional part as the main factor in their research with the

impact on advertising. It could be specified that the authors discussed the implications

of the use of emotional expressions, titles of advertisements, consumer knowledge of

the brand to promote emotions in the consumer and help the purchasing decision

process.

However, the emotions are present throughout the process of consumer behavior, but

it is vital determinate what the origin of this is. Pelsmaeker et al. (2017) explained the

relationship of emotions in the begging of the process of consumer intention, and they

determined the relevance of applying an evaluation before recognizing the need.

Emotions can indeed be positive and negative depending on the moment or value.

However, some researchers in recent years were working only for positive emotions

because only these matter. Wen, Hu, & Kim (2018) examined the effect of individual

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Doctoral Thesis


culture on positive emotions for the recommendation intention. Finally, positive

emotions are the principal element to determine the satisfaction of the consumer (Io,

2017).

Williams & Aaker (2002) believed that when individuals are exposed to mixed

emotions, they influenced the individual´s attitudes in general. They also demonstrated

that the detonation of emotions with duality (e.g., sadness and happiness) is less prone

to form an attitude towards their behavior. Haws & Winterich (2013) described the

factors to measure the attitude toward eating directly to these items: pleasure, enjoy,

satisfied and good taste. However, the consumer can have an attitude toward the brand

and not for eating. That reason describes Aggarwal & McGill (2012) finding of what

consumers like, think, admire and fit in their life is a good positive attitude that helps

to stimulate the intention. This study proposed two constructs, one for eating the

hamburger and the other for the brand.

Thus, the following hypothesis can be derived:

H5. Positive anticipated emotions will positively influence attitude toward the

brand.

H6. Positive anticipated emotions will positively influence attitude toward

eating a hamburger.

H7. Positive anticipated emotions will positively influence the intention to buy.

Therefore, seven hypotheses were tested in this research and based on the discussion

above (see Figure 4.1), considers seven proposed effects: 1) attitude toward the brand

on purchase intention, 2) attitude toward eating hamburger on purchase intention, 3)

food values on attitude toward the brand, 4) food values on attitude toward eating

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hamburger, 5) positive anticipated emotions on attitude toward the brand, 6) positive

anticipated emotions on attitude toward eating hamburger, and 7) positive anticipated

emotions on purchase intention. Thus, all the effects correspond to a new model for

understanding better the purchase intention in fast food restaurants.

Figure 4.1 Model development.

4.2 Materials and Methods

This study utilized partial least squares-structural equation modelling (PLS-SEM) to

examine the impact of the food values, emotions anticipated and attitudes on purchase

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Doctoral Thesis


intention (see Table 4.1 for technical details). The proposal was to estimate a model

that includes a mix of factors and composites using the PLS algorithm procedure

(Sarstedt, Hair, Ringle, Thiele, & Gudergan, 2016). The idea was to maximize the

explained variance of all dependent variables used in the research model. In this case,

the research intent was to know the predictor variable and to identify possible drivers

(J. Hair, Hollingsworth, Randolph, & Chong, 2017; Shmueli, Ray, Velasquez Estrada,

& Chatla, 2016). Therefore, the independent variables that the literature reports as

important predecessors of purchase intention were also included.

Table 4.1 Technical details.

Universe Residents in Puebla State in México

Sample unit People over 17 years old and buying fast food

Information collection method Personal survey

Sample error ± 4.335




Period of information collection January 26—May 23 (2018)

Language Spanish

4.2.1 Data collection

The data was collected from Puebla City in Mexico with a consumer survey of 512

participants. Participation was voluntary and all of them completed the questionnaire.

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4.2.2 Statistics analysis

The study used structural equation modeling (SEM) to test the conceptual model with

SmartPLS 3.0 software. According to Streukekens and Leroi-Werelds (2016)

(Streukens & Leroi-Werelds, 2016), this study used partial least squares (PLS) with a

10,000 subsample bootstrapping procedure and the same software to know if the

relationship was supported or not with the results. In the beginning, this model was

composted from 34 items reduced to 28 items in 5 constructs. From there, no

preliminary empirical parameters for this particular market were found.

4.2.3 Questionnaire development

The questionnaire was constructed and divided into five sections: a) food values, b)

positive and negative anticipated emotions, c) attitude toward the brand, d) attitude

toward eating a hamburger, and e) purchase intention (see Table 4.2). The first table

shows the questionnaire section by source and the second explain details on how to

measure each variable.

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Doctoral Thesis


Table 4.2 Questionnaire sections.

Latent

variable

Observed

variables Definition Source

Food values

are general

food attributes

that

consumers

believed were

relatively

more

important

when

purchasing

food

Appearance Extent to which food looks appealing

Lusk

(2011)

Convenience Ease with which food is cooked and consumed

Environmental Effect of food production on the environment

Fairness The extent to which all parties involved in the

production of the food equally benefit

Naturalness Extent to which food is produced without modern

technologies

Nutrition Amount and type of fat, protein, vitamins, etc.

Origin Where the agricultural commodities were grown

Price The price that is paid for the food

Safety Extent to which consumption of food will not cause

illness

Taste Extent to which consumption of the food is appealing

to the senses

Tradition Preserving traditional consumption patterns

Positive and

negative

anticipated

emotions

Contentment If I can go to eat a hamburger in fast-food restaurants

the next month, I feel contentment

Adapted

from

Bagozzi

and

Dholakia

(2006)

Delighted If I can go to eat a hamburger in fast-food restaurants

the next month, I feel delighted

Excited If I can go to eat a hamburger in fast-food restaurants

the next month, I feel excited

Proud If I can go to eat a hamburger in fast-food restaurants

the next month, I feel proud

Satisfied If I can go to eat a hamburger in fast-food restaurants

the next month, I feel satisfied

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Selfassured If I can go to eat a hamburger in fast-food restaurants

the next month, I feel self-assured

Attitude

toward the

brand (ATB)

ATB1 Like the brand Aggarwal

and

McGill

(2012)

ATB2 Admire the brand

ATB3 Fit in your life the brand

Attitude

toward eating

a hamburger

(ATEH)

ATEH1 Eating the hamburger would be pleasurable Adapted

from Haws

and

Winterich

(2013)

ATEH2 I would enjoy eating the hamburger

ATEH3 If I eat a hamburger, it would be satisfying for me

ATEH4 If I eat a hamburger because of the good taste it has

Purchase

intention

PI1 You probably buy products in fast-food restaurants Adapted

from Chiu,

Hsieh, and

Kuo

(2012),

Diallo

(2012)

PI2 I would consider buying a product in fast-food

restaurants if I need a product of this type

PI3 It is possible to buy a product in fast-food restaurants

PI5 The probability that you consider buying in fast-food

restaurants is high

The food values utilized a Likert scale 1 - 5 (1 = not at all important, to 5 = extremely

important). The scale was adapted from 7-points to 5-points, because it was planned

to explain each item as a formative construct. It is better to get an answer from the

consumer on the assumption that some items do not have a relation with the construct.

Positive and negative anticipated emotions applied a Likert scale 1 - 7 (1 = none, to 7

= severe). From the original items, it supported the positive emotions because the

negatives did not have an impact, and did not comply with the test of validity and

reliability. It deleted the emotions for: glad, relief and happy for the reason to have

multicollinearity and the VIF factor > 3.2. Also, it used the 7-point Likert scale as the

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Doctoral Thesis


author marked it. According to Becker & Ismail (2016), it is possible to use different

Likert scales within the same model. In the attitude toward the brand (ATB) it used a

Likert scale 1 - 5, (1 = strongly disagree, to 5 = strongly agree). From the original

contribution, it supported only the positive items because the weights were weak (item

4 “shame” and 5 “avoidance”). It changed the inverse items for the nature of the scale.

For the attitude toward eating a hamburger (ATEH) it was handled with a Likert scale

1 - 5, (1 = strongly disagree, to 5 = strongly agree). These items were adapted to the

specific product (in this case, hamburger). The variable purchase intention was

measured by a Likert scale 1 - 5, (1 = strongly disagree, to 5 = strongly agree). PI4 was

excluded because it had multicollinearity with PI3. The item was "I would buy in

McDonald´s next time".

All the constructs were reflective, not including food values. The construct formed the

interpretations depending on the dependent variable. Hence, the formative indicators

may show non-significant. Also, the indicators were correlated with other indicators

in the model proposal (Diamantopoulos & Papadopoulos, 2010). Similarly, all the

formative indicators required a census of all items for the construct because each one

(it can be negative or positive) was formed into the complete variable. Even the

negative influences in the consumer were one item that needed to be taken care of

(Jarvis, MacKenzie, & Podsakoff, 2003). Finally, the overall fit of this model does not

matter; the other covariances like the exogenous variables are outside the model

proposal and all the items are independent with themselves, according to Jarvis et al.

(2003).

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4.3 Results

The development model was constructed on an amalgamation of items, concepts,

models, effects and principles about two parts: functional and emotional. This model

was also composited about a series of research studies around four exceptional areas:

1) food values, 2) attitude toward the brand, 3) attitude toward eating a hamburger,

and 4) positive anticipated emotions. All were within the proposal to better explain the

purchase intention in fast food restaurants in Mexico.

To assess the goodness of model fit, the root mean square residual (SRMR) was

utilized. According to Hu & Bentler (1998) and Hu & Bentler (1999), SRMR < 0.08

is a good fit for SRMR. This model has a SRMR=0.049<0.08 SRMR criteria; these

measures found that this model has a good fit with the parameters mentioned before.

The normed fit index (NIF) results in values from 0 to 1, and the closer to 1, the better

the fit (Bentler & Bonett, 1980). In this model, the NIF was .899 and represented an

acceptable fit.

To get confidence in this model, reliability and construct validity testing were carried

out. Cronbach´s alpha coefficient was accepted for all the constructs, having a value

greater than .7 ( Hair, 2010). The rho_A value was reflected regularly if this index was

larger than 0.7 (Werts, Linn, & Jöreskog, 1974). The composite reliability (CR) values

under 0.6 indicated a deficiency of internal consistency reliability (Hair, 2017). The

AVE of each construct was above the tolerability value 0.5 (Fornell & Larcker, 1981;

Huang, Wang, Wu, & Wang, 2013) (see Table 4.3).

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Doctoral Thesis


Table 4.3 Validity testing.

Cronbach´s alpha

coefficient rho_A

Composite

reliability (CR)

Average variance

extracted (AVE)

Attitude toward

eating a hamburger 0.847 0.862 0.897 0.687

Attitude toward the

brand 0.822 0.836 0.893 0.736

Positive anticipated

emotions 0.916 0.921 0.934 0.704

Purchase intention 0.895 0.896 0.927 0.760

As a final point, the discriminant validity of constructs showed the factor loading

indicators on the assigned construct. Therefore, they had to be above all loading of

other constructs (in the same column) with the condition that the cut-off value of factor

loading was higher than .70 (Fornell & Larcker, 1981). In addition, the model proved

satisfactory reliability with, convergent and discriminant validity. After this step, it

was necessary to test the discriminant validity of constructs. According to Fornell &

Larcker (1981), with the correlation coefficient of the two dimensions less than the

square root of the AVE, two dimensions were understood to have discriminant validity

because of AVE >0.5 (see Table 4.4).

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Table 4.4 Association testing.

Attitude

toward

Eating a

Hamburger

Attitude

toward the

Brand

Food

Values

Positive

Anticipated

Emotions

Purchase

Intention

Attitude toward

eating a

hamburger

0.829

Attitude toward

the brand 0.538 0.858

Food values 0.431 0.444 Formative

Positive

anticipated

emotions

0.482 0.544 0.401 0.839

Purchase intention 0.537 0.665 0.407 0.544 0.872

The study confirmed the hypothesis with path coefficient, standard error, t-value and

p-value (see Table 4.5). It was concluded that all the hypotheses planted were

supported and positive to predict the purchase intention with a high level, even though

the study observed some differences about each association. The first force is the

association between attitude toward the brand on purchase intention had the best path

coefficient (β=.447). Moreover, the results showed that attitude toward eating a

hamburger also had importance to purchase intention (β=.197). However, the other

association to predict purchase intention was throughout the positive anticipated

emotions and for this model was (β=.206), more than attitude toward eating a

hamburger.

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Doctoral Thesis


The great force to constitute the attitude toward the brand was with the construct

positive anticipated emotions (β=.436). It was because in comparison, the attitude

toward eating a hamburger only has β=.368. Something relevant was about the impact

on attitudes about the food values, where it had some consideration to attitude toward

eating a hamburger (β=.270), but not much to the brand (β=.284).

Some reflections about all the hypotheses proposed are the level of significance, where

p-value <.001 with the 99%; it means that these study results were statistically

significant.

Also, the H5 line of positive anticipated emotions to attitude toward the brand (β=.436,

t=10.126, p=<0.001) and the H1 line of attitude to purchase intention (β=.447,

t=10.849, p=<0.001) indicated an abundant positive effect to form the purchase

intention; this was the best way to predict it. Table 4.5 shows that in all the relations,

t-value≥1.96 and p-value≤0.05; thus, this model supported all the hypotheses with high

path coefficients and t-values. Hence, outer model loadings were highly significant. In

addition, f2 was utilized to confirm the hypotheses null in the model and the outcomes

supported each hypothesis but with different effects from weak <.15 to large >.15

(Hair, Sarstedt, Hopkins, & G. Kuppelwieser, 2014). All q2 are above zero, which

supports the model presenting in the Figure 4.2 (Hair, 2017).

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Table 4.5 Hypothesis testing and path coefficients.

Beta Standard

error t-value

p-

value f2 q2 Supported

H1

Attitude

toward the

brand ->

Purchase

intention

0.447*** 0.041 10.849 0.000 0.249 0.134 Yes

H2

Attitude

toward eating

a hamburger

-> Purchase

intention

0.197*** 0.043 4.574 0.000 0.053 0.030 Yes

H3

Food values -

> Attitude

toward the

brand

0.270*** 0.042 6.447 0.000 0.095 0.050 Yes

H4

Food values -

> Attitude

toward eating

a hamburger

0.284*** 0.043 6.608 0.000 0.097 0.052 Yes

H5

Positive

anticipated

emotions ->

Attitude

toward the

brand

0.436*** 0.043 10.126 0.000 0.248 0.146 Yes

H6 Positive

anticipated 0.368*** 0.040 9.167 0.000 0.163 0.088 Yes

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Doctoral Thesis


emotions ->

Attitude

toward eating

a hamburger

H7

Positive

anticipated

emotions ->

Purchase

intention

0.206*** 0.050 4.129 0.000 0.057 0.030 Yes

Note: n=10,000 subsamples; ***p<.001; R2 (Attitude toward the brand =0.357;

Attitude toward eating=0.300; Purchase intention=0.515); q2=Predictive relevance

calculated ((R-Sq included)-(Q-Sq excluded))/(1-R-Sq included).

Esposito Vinzi, Chin, Henseler, & Wang (2010) stated formative constructs need not

be correlated between them. Also, the construct needs to be supported with the

theory about food values. Similarly, the PLS algorithm produced loadings for

reflective construct and weight for formative. Moreover, the study used the loadings

and weights indicator for each construct by nature.

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Figure 4.2 PLS analysis results.

Figure 4.2 indicates the formative construct (food values), and inside the construct, the

best items are taste and tradition (.490; .380). On the other hand, the food values show

negative loading with environment and nutrition (-.256; -.233). These facts do not have

a position for the food value. Also, the model indicated that the emotions of

contentment, excited and satisfied are the best loadings in the model (.869, .856, .843).

It is distinguished that R2 (ATEH) is .357 higher than ATB (.300). Additionally, R2

(PI) is .515, signifying that both attitudes toward eating and the brand plus positive

anticipated emotions explain 51% of purchase intention. Even though R2-ATEH and

R2-ATB are weak, the R2-purchase intention is substantial (Hair et al., 2014).

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Doctoral Thesis


4.4 Discussion

All the hypotheses proposed were supported and confirmed. It accepted the difference

by two types of attitudes: one of them toward the brand and the other toward eating a

hamburger. Also, it showed the gap between the beta indicators with .250 to predict

the purchase intention. The attitude toward the brand got the first place in the

hypotheses. Based on the previous study, the theory and empirical research suggested

that attitude toward the brand will positively influence the intention to buy. After the

results, it confirmed the positive influence and on the same road with other studies. In

this case, it corroborated with the results of Hwang et al. (2011) were mentioned that

the affective responses positively influence to brand attitudes and purchase intention.

The attitude toward eating had the right place in the final model. This hypothesis was

confirmed, and the values obtained help to explain with more percentage the purchase

intention. Others authors affirm the importance to investigate the eating behavior from

to get knowledge about the positive or negative predisposition to eat (Chen, 2009;

Ghoochani et al., 2018). The hypotheses related to food values were an essential

variable in this model, i.e., the relationship of this variable to both attitudes. At this

point, it demonstrated that the food values could be impacted in a different way to each

attitude. It validated the influence of food values affecting indirectly on the purchase

intention. With this information, it led to some discussion to add more food values and

to get an effect indirect to purchase intention. For example, these results match to Lang

& Lemmerer (2019) which affirm that personal values impact on forming a food

attitude. Last, the positive anticipated emotion positively influenced attitude toward

the brand, attitude toward eating, and intention to buy a hamburger. The results are

consistent with previous research; with assert that emotion is an irreplaceable variable

to try predicting the purchase intention. Positive anticipated emotion is a significant

140


variable, which participates in three hypotheses addressing to attitude toward the

brand, attitude toward eating a hamburger and purchase intention. This confirms

findings in other studies (Aggarwal & McGill, 2012; Evers, Adriaanse, de Ridder, &

de Witt Huberts, 2013; Jiang, King, & Prinyawiwatkul, 2014).

Managerial implications are confirmations derived from this research. First of all,

managers of fast food restaurants have to focus on the purchase intention of consumers.

The findings support that purchase intention is more influenced by attitude toward the

brand than by attitude toward eating a hamburger. Subsequently, the food value does

not impact very strongly, rather than the positive anticipated emotions. The managers

need to study how powerful is each emotion as contentment, excited and satisfied

before thinking about eating something at McDonald´s. Also, the best values to build

into the product are the taste and tradition. Hence, in this case, the managers need to

investigate about preferences, tastes and culture around the consumption in the fast

food restaurants. In that way, they need to prefer a strategy with a focus to increase

and improve the value of the brand toward the brand equity oriented into the consumer.

Correspondingly, positive anticipated emotions do not have a good association directly

with purchase intention. This explains that without an attitude toward eating a

hamburger or the attitude toward the brand, the consumer does not perceive the

intention to buy a hamburger at McDonald´s.

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Doctoral Thesis


4.4.1 Limitations and future orientations

There are limitations and suggested future lines of research. First of all, the sample

should be increased to raise the level of confidence and lower the level of sampling

error. Alternatively, it is recommended to add other variables related to TPB as

perceived control, perceived difficulty and subjective norms on purchase intention.

Finally, it is suggested to apply these surveys in other cities, products, and brands to

know if there are significant differences between the samples.

4.5 Conclusions

The goal for this study was building a development and testing model and having one

comprehensive model about the purchase intention. The study planted a model with

the importance of the functional and emotional aspects through their effects on two

attitudes. This model is an approximation to better explain the purchase intention. The

food values have a low position on attitude toward the brand and attitude toward eating

a hamburger. In the other hand, anticipated positive emotions have more relevance on

attitudes, especially the attitude toward the brand and to purchase intention.

The positive food values are taste and tradition in fast food consumers. This model

provides information to fast food restaurants to pay attention to constantly evaluate the

taste that has the consumers’ favor and to explore insights about a different perception

of taste in the hamburger. Also, the tradition is significant because it includes and

preserves traditional consumption patterns, since children families and reference

groups help to educate this kind of consumption. In the other view, the consumer does

not care about the nutrition of the hamburger against the knowledge of the brand. This

142


confirms the results from Barone, Rose, Manning, & Miniard (1996) that examined

the cause to form incorrect conclusions about the product. In this case, the consumer

does not give value to the type of fat, proteins, vitamins and carbohydrates that the

hamburgers have. This demonstrates the lack of sensitivity and knowledge of healthy

and responsible consumption.

Similarly, it is happening with the environment value where the most significant

weight in the variable of food value is. The consumer does not care if the burger is

produced while taking care of the environment. The problem of having production for

the environment and pollution does not see some or any benefit knowing how the food

was manufactured. So the adequacy of practices in favor of the environment and eco-

friendly consumption is not significantly crucial for attitude or purchase intention.

It was also shown that positive anticipated emotions form the best way to explain the

purchase intention. First of all, it was verified that the anticipated negative emotions

did not show any relevant data that included that variable within the model.

Subsequently, the items with the greatest loading were analyzed, and the results were

positive anticipated emotions like contentment, delighted, excited, proud, satisfied and

self-assured. If the consumer has one type of this emotion, it is probably to have a good

level of attitude toward the brand and to get a purchase intention.

For this reason, the results of the study confirm the existence of a strong relationship

between attitudes toward the brand on purchase intention by way of anticipated

positive emotions in the consumer of McDonald´s. This proves as in previous

literature, that emotions are a necessary measure of the decision-making process of the

consumer (Bagozzi, Dholakia, & Basuroy, 2003).

143

Doctoral Thesis


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Zhang, Y., Jing, L., Bai, Q., Shao, W., Feng, Y., Yin, S., & Zhang, M. (2018).

Application of an integrated framework to examine Chinese consumers’

purchase intention toward genetically modified food. Food Quality and

Preference, 65, 118–128. https://doi.org/10.1016/j.foodqual.2017.11.001 156


Zhao, X., Deng, S., & Zhou, Y. (2017). The impact of reference effects on online

purchase intention of agricultural products: The moderating role of

consumers’ food safety consciousness. Internet Research, 27(2), 233–255.

https://doi.org/10.1108/IntR-03-2016-0082

157



5.1 Discussions

The food sector underscores the importance of studies aimed at ascertaining consumer

perception. Thus, this thesis includes three studies aimed at understanding the value of

consumer behaviour in strategic marketing research.

The analysis of the first study provides evidence of the importance of consumer

problems. This proves the relevance of the "consumer" as the central axis of research

throughout the research period. Similarly, the "product" is also positioned as a next

factor of importance. The first study provides value for academics, researchers and

professionals within the area of marketing sciences by tracking and identifying the

most relevant publications with respect to time periods and topics. By providing

graphs, readers can quickly identify those articles that made significant contributions

in the field or locate specific publication niches. This work also illustrates how reviews

of marketing literature can be carried out effectively while reducing time spent. The

main theme, "customer choice," plays a strategic role in establishing a link between

consumers and purchasing decisions. Two other main themes of interest were "strategy

development" and "pricing programs". This provides evidence for the idea that pricing

policies are relevant to contemporary marketing, bearing in mind that pricing policies

encompass concepts such as "action indicators", "performance measures" and

"profitability metrics". The results provide partial support for the popularity that

Customer Relationship Management (CRM) has gained in recent years. In this sense,

the topics most related to CRM are "value added", "orientation" and "service". Here,

the importance of long-term customer relationships, which is a fundamental concept

in marketing science, is also highlighted. “Emotional marketing" is another major

theme that recognizes the generation of knowledge in this discipline by researching

the emotions of individuals. This work also contributes to the discussion of how

161

Doctoral Thesis


literature reviews can be conducted within marketing science or in other disciplines.

The main objective is resolved by proposing useful methods for classifying

publications according to content similarities. The methods presented here could be

used as general guidelines for authors and researchers who are interested in conducting

systematic literature reviews. By identifying the specialized vocabulary used in this

discipline and then incorporating it into their papers, authors can be assured that they

are at the forefront of the use of modern vocabulary. In the same vein, it was found

that "consumers" and "customers" are the central topics of marketing research journals

and that the concept of "product" has become a fundamental concept where new

consideration emerges towards the development of new products and their interactions

with the consumer. According to the analysis it was detected that the word "food" is

one of the most worrying sectors for marketing researchers. This generated the

continuation with the following two studies.

For the second study, all the hypotheses proposed in this study have been confirmed,

evidencing positive and significate relations between the construct proposed. First,

with regard to the influence of food values on attitude, this research has revealed the

importance that consumers attach to each of the values of the proposed scale. So taste,

tradition, appearance and convenience are the best valued. Previous studies have also

emphasized these values and as in this research, the price is not one of the values that

have much weight. The latter is in line with the product category, since its cost is

reduced. It is noteworthy that the weights of the values “environmental impact”,

"nutrition" and "origin" (although to a very minor extent) are negative. In relation to

the “environmental impact” food value, it could be an indication that consumers think

that the intake of a hamburger has no impact on the environment. With regard to the

"nutrition" food value, it may be motivated by the fact that it is considered an

162


"unhealthy" product with a high amount of calories. In relation to the “origin” food

value, maybe it could be a hint that consumers do not care about the origin of this type

of food. Second, the importance of hedonic and utilitarian benefits should be stressed.

The items that value these benefits are consistent as previous research. However, it

should be noted that "convenience" has been valued as a hedonic benefit. Sometimes,

the “delight” of a meal, or dinner, can be frustrated by the waiting times to be attended,

to the delay in providing the service (once requested). All these aspects are very well

valued for the enjoyment of the consumers. A benefit that has a utilitarian character -

convenience - can be converted into a hedonic benefit. As for the utilitarian benefits,

all of them have been very well valued by the consumers. Highlighting here as a

benefit, which can be considered hedonic-appearance-has been valued as a utilitarian

benefit. This result is consistent with the value perceived by a consumer when

purchasing any product or service. The consumer when assessing the product, not only

takes into account its cost, but also how the product is presented to be consumed, so it

becomes a utilitarian benefit. In addition, both hedonic and utilitarian benefits,

positively support the formation of positive attitudes towards hamburger consumption

and specifically those sell by McDonald’s. It is noteworthy of this last aspect, since it

is not the same to eat a product such as hamburgers when the whole process is

controlled by the consumer, or when attending a restaurant (not fast-food), which has

some quality indicator (Michellin stars, number of forks, etc.), when consumed at

McDonald's, where no quality process is controlled and is considered a fast-food

restaurant. Additionally, this mediating effect of the attitude in the relation of

utilitarian / hedonic benefits and intentions should be highlighted, which shows the

weight that both benefits have on intentions, this weight being higher for hedonic

benefits. This is a novelty, since until now these mediating effects had not been tested.

Finally, it has been observed how there is an association between attitudes toward

163

Doctoral Thesis


eating hamburgers and purchase intention. This would be a consistent finding

according to the previous research.

The objective of the last study was to construct a development and testing model,

where a complete model of explanation of consumers' purchase intention is generated.

The study planted a model with the importance of functional and emotional aspects

through their effects on two types of attitudes. Food values have a low position in

brand attitude and hamburger attitude. On the other hand, the positive emotions

anticipated have more relevance in attitudes, especially the attitude towards the brand

and the intention to buy. The positive values of food are taste and tradition in fast food

consumers. This model provides information to fast-food restaurants to pay attention

to constantly assessing the taste value of consumers and to explore ideas about a

different perception of taste in the hamburger. In addition, tradition is significant

because it includes and preserves traditional consumption patterns, as children's

families and reference groups help to educate this type of consumption. From the other

point of view, the consumer does not care about the nutrition of the hamburger versus

brand awareness. In this case, the consumer does not value the types of fats, proteins,

vitamins and carbohydrates that burgers have. This shows the lack of sensitivity and

knowledge of healthy and responsible consumption. Similarly, it is also happening

with the environmental value where the most significant weight is in the food value

variable. The consumer does not care if the hamburger is produced while caring for

the environment. The problem of having production for the environment and pollution

sees no benefit or no benefit in knowing how the food was manufactured. Therefore,

the adequacy of practices in favor of the environment and consumption respectful of

the environment is not significantly crucial to the attitude or intention to purchase. It

was also shown that positive anticipated emotions are the best way to explain the

164


purchase intention. Firstly, it was verified that the expected negative emotions did not

show any relevant data that included that variable within the model. Subsequently, the

items with the highest loads were analyzed, and the results were anticipated positive

emotions such as satisfaction, charm, emotion, pride and security. If the consumer is

going to have one of these emotions, he is likely to have a good level of attitude

towards the brand and then develop a purchase intention. For this reason, the results

of the study confirm the existence of a strong relationship between attitudes towards

the brand in the intention to buy through positive emotions anticipated in the consumer

of fast food restaurants. This demonstrates that emotions are a necessary measure of

the consumer's decision-making process.

The objectives of this research were met throughout the research process. The general

objective was to analyze consumer behavior in the food consumption decision process.

This implied strongly analyzing the impact of food on consumer behavior in two axes:

scientific research and empirical research. This fulfilled the task of performing a

content analysis over a nine-year period of the journals with the greatest impact factor

in the marketing discipline in order to identify the research topics according to science.

It was also determined the impact of food values in their transformation towards

hedonic and utilitarian benefits, within the output variables such as consumption

attitude and purchase intention. Finally, the explanation of purchase intention was

increased by adding variables related to the hedonic part of consumption, such as

positive anticipated emotions. A more punctual model of their purchasing behavior

was presented, separating different types of attitudes, one towards the act of eating and

the other towards the brand.

The highlights of this Doctoral Thesis can be summary in six result-oriented points: 1)

The consumers and customers are the main research topics in marketing journals,

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Doctoral Thesis


which show a growth in consumer buying behavior; 2) Unlike previous periods, the

product has been conferred as an essential factor to apply a new consideration to the

design of new products in line with consumer information; 3) The positive influence

of food values on the incorporation of the utilitarian and hedonic benefits of

consumption was also confirmed; 4) The great impact was the attitude toward the

brand rather than the attitude toward eating oneself was detected in the model of

predicting the consumer's purchase intention; 5) Food values and anticipated positive

emotions positively influence brand attitude, which in turn affects the consumer's

buying intention; 6) Anticipated positive emotions have a stronger impact than food

values, and it is reaffirmed the best way to explain the purchase intention is through

the brand attitude rather than the food attitude.

5.2 Business implications

According to the business implications, fast-food companies should keep in mind that

despite the heavy investments in advertising they have made, consumers still think that

their hamburgers are “unhealthy” (negative weight of food values), that it is a product

of convenience and they have managed to capture the consumer for those values that

are closely related to the act of consumption, such as: taste, tradition, appearance and

convenience. It can be believe that in the long term, these companies should change

their advertising message and try to emphasize healthier values. The new consumer

segments are more informed, they look at the nutritional components and above all

they value eating “healthy” products that do not harm the environment, although for

this they must pay a premium price. This change in strategy may be favored, due to

the importance of both hedonic benefits (enjoying food, delighting) and utilitarian, in

the formation of attitudes.

166


Also, managers of fast food restaurants have to focus on the purchase intention of

consumers. The findings support that purchase intention is more influenced by attitude

toward the brand than by attitude toward eating a hamburger. Subsequently, the food

values do not impact very strongly, rather than the positive anticipated emotions. The

managers need to study how powerful is each emotion as contentment, excited and

satisfied before thinking about eating something at McDonald´s. Also, the best values

to build into the product are the taste and tradition. Hence, in this case, the managers

need to investigate about preferences, tastes and culture around the consumption in the

fast food restaurants. In that way, they need to prefer a strategy with a focus to increase

and improve the value of the brand toward the brand equity oriented into the consumer.

Correspondingly, positive anticipated emotions do not have a good association directly

with purchase intention. This explains that without an attitude toward eating a

hamburger or the attitude toward the brand, the consumer does not perceive the

intention to buy a hamburger at McDonald´s.

5.3 Future lines of study

This Doctoral Thesis has some future lines of study for each objective proposed in this

research. In the first study of the identification of research topics in marketing science,

the following points are proposed.

- It is recommended to build another analysis period 2015-2024 and to be able to

visualize the different ones with the previous period in terms, topics and dimensions

that have made marketing science consolidate.

167

Doctoral Thesis


- Add more abstracts of other journals of the same category to the database to enrich

the vocabulary and reaffirm or reject the findings found in the first study.

- Analyze another category of journals focused on food marketing topics to find the

main topics in food consumption behavior.

For the second and third study it is recommended to add other variables related to TPB

as perceived control, perceived risk and subjective norms on purchase intention.

Finally, it is suggested to apply these surveys in other cities, products and brands to

know if there are significant differences between the samples.

168

Appendices

Appendices

Appendice 1. Survey A

Estimado Encuestado:

En esta encuesta pretendemos obtener su opinión respecto al consumo de comida

rápida. Este estudio es conducido por la Universidad de Castilla-La Mancha. Las

opiniones que usted nos proporcione serán clasificadas como confidenciales y serán

utilizadas solamente para propósitos de investigación académica.

En el apartado siguiente, por favor indica cuan tan importante son los siguientes

factores cuando acudes a comprar algún producto de Mcdonalds.

Valore LOS ATRIBUTOS QUE MÁS APRECIA DE LA COMIDA DE

MCDONALD. Siendo (1) la valoración más baja y (5) la valoración más alta.

Naturalidad (producto producido sin tecnologías modernas) 1 2 3 4 5

Sabor del producto 1 2 3 4 5

Precio 1 2 3 4 5

Seguridad (de que el alimento no causará ninguna enfermedad) 1 2 3 4 5

Conveniencia (facilidad con que el alimento es cocinado o consumido) 1 2 3 4 5

Nutrición (cantidad y tipo de grasas, proteínas, vitaminas, etc.) 1 2 3 4 5

Tradición (conservación de las pautas de consumo tradicionales) 1 2 3 4 5

Origen (donde se han producido las materias primas de los alimentos) 1 2 3 4 5

Justicia (el grado en el que todas las partes implicadas en la producción del

alimento salen igualmente beneficiadas)

1 2 3 4 5

Apariencia (grado en el que el alimento parece atractivo, presentación) 1 2 3 4 5

Impacto medioambiental (efecto de la producción del alimento en el medio

ambiente)

1 2 3 4 5

171

Doctoral Thesis


Valoración BENEFICIOS de McDonald. Siendo (1) la valoración más baja y (5) la

valoración más alta.

Siento que la naturalidad de los productos de me causa placer. 1 2 3 4 5

Siento que el sabor de los productos me da placer. 1 2 3 4 5

El precio de los productos me da placer. 1 2 3 4 5

La seguridad e inocuidad del alimento me da placer. 1 2 3 4 5

La comodidad de consumo y de preparación de los alimentos es placentera. 1 2 3 4 5

Siento que la nutrición de los productos es placentera. 1 2 3 4 5

Siento que la tradición de Mcdonald me causa placer. 1 2 3 4 5

El origen de los productos me da placer al consumir el producto. 1 2 3 4 5

Al consumir favoreciendo al comercio justo me causa placer. 1 2 3 4 5

El aspecto y presentación de los productos me da placer. 1 2 3 4 5

Siento que el impacto al medio ambiente al producir algún producto me da placer. 1 2 3 4 5

172

Appendices

Valoración BENEFICIOS de McDonald. Siendo (1) la valoración más baja y (5) la

valoración más alta.

La naturalidad del producto me ayuda quitarme el hambre. 1 2 3 4 5

Siento que el sabor me ayuda a quitarme el hambre. 1 2 3 4 5

El precio es adecuado para la necesidad de comer que tengo. 1 2 3 4 5

La seguridad e inocuidad del alimento me ayuda a satisfacer mi necesidad de

comer.

1 2 3 4 5

La comodidad de consumo y de preparación es útil para mí necesidad de comer. 1 2 3 4 5

La nutrición es útil a lo que necesito en un momento determinado. 1 2 3 4 5

Siento que la tradición es útil y necesaria. 1 2 3 4 5

El origen de los productos de la hamburguesa los creo útiles y elementales al

consumir el producto.

1 2 3 4 5

Al consumir favoreciendo al comercio justo es útil y necesario. 1 2 3 4 5

El aspecto y presentación del producto es útil y necesaria.

1 2 3 4 5

Siento que el impacto al medio ambiente que se tiene al producir el alimento es

útil y necesario.

1 2 3 4 5

En el apartado siguiente, por favor indica cuan tan fuerte estás de acuerdo o

desacuerdo con las siguientes afirmaciones de tu primera elección de fast food de

acuerdo a su producto principal. Evalúa cada ítem usando una escala de 5 puntos

donde: 1=Extremadamente desacuerdo, 2=Ligeramente desacuerdo, 3=neutral,

4=Ligeramente de acuerdo, y 5=Extremadamente de acuerdo.

173

Doctoral Thesis


Actitud a través del consumo de alimentos

1. Al comer la hamburguesa es placentero. (1) (2) (3) (4) (5)

2. Me gustaría divertirme comiendo la hamburguesa. (1) (2) (3) (4) (5)

3. Si como la hamburguesa sería satisfactorio para mí. (1) (2) (3) (4) (5)

4. Si como una hamburguesa es por el buen sabor que tiene. (1) (2) (3) (4) (5)

Actitud a través de la marca McDonald´s.

1. Me gusta la marca. (1) (2) (3) (4) (5)

2. Admiro a la marca. (1) (2) (3) (4) (5)

3. La marca encaja en mi vida. (1) (2) (3) (4) (5)

4. Me da vergüenza que me vean con la marca. (1) (2) (3) (4) (5)

5. Evito estar con la marca. (1) (2) (3) (4) (5)

Intención de compra en McDonald´s.

1. Es probable que compre productos de McDonald´s. (1) (2) (3) (4) (5)

2. Consideraría comprar el producto de McDonald´s si necesito un producto de este

tipo. (1) (2) (3) (4) (5)

3. Es posible comprar algún producto de McDonald´s. (1) (2) (3) (4) (5)

4. Compraría en McDonald´s la próxima vez. (1) (2) (3) (4) (5)

5. La probabilidad de que considere comprar en McDonald´s es alta. (1) (2) (3) (4)

(5)

174

Appendices

Sexo:

1. Femenino 2. Masculino Edad:

Ciudad:

Muchas gracias por su cooperación y participación.

Estado Civil

Soltero/a 1

Casado (a) sin hijos 2

Casado con hijos menores de 15 años; 3

Casado con hijos mayores de 16 años 4

Divorciado (a) sin hijos 5

Divorciado con hijos menores de 15 años; 6

Divorciado con hijos mayores de 16 años 7

Viudo/a 8

Nivel de Ingresos Mensual

Hasta 6000 1

6001-9000 2

9001-12000 3

12001-15000 4

+15000 5

Nivel de estudios

Hasta Secundaria 1

Preparatoria, Bachiller o carrera Técnica 2

Licenciatura 3

Postgrado 4

175

Appendices

Appendice 2. Survey B

Estimado Encuestado:

En esta encuesta pretendemos obtener su opinión respecto al consumo de comida

rápida. Este estudio es conducido por la Universidad de Castilla-La Mancha. Las

opiniones que usted nos proporcione serán clasificadas como confidenciales y serán

utilizadas solamente para propósitos de investigación académica.

En el apartado siguiente, por favor indica cuan tan importante son los siguientes

factores cuando acudes a comprar algún producto de Mcdonalds.

Valore LOS ATRIBUTOS QUE MÁS APRECIA DE LA COMIDA DE

MCDONALD. Siendo (1) la valoración más baja y (5) la valoración más alta.

Naturalidad (producto producido sin tecnologías modernas) 1 2 3 4 5

Sabor del producto 1 2 3 4 5

Precio 1 2 3 4 5

Seguridad (de que el alimento no causará ninguna enfermedad) 1 2 3 4 5

Conveniencia (facilidad con que el alimento es cocinado o consumido) 1 2 3 4 5

Nutrición (cantidad y tipo de grasas, proteínas, vitaminas, etc.) 1 2 3 4 5

Tradición (conservación de las pautas de consumo tradicionales) 1 2 3 4 5

Origen (donde se han producido las materias primas de los alimentos) 1 2 3 4 5

Justicia (el grado en el que todas las partes implicadas en la producción del

alimento salen igualmente beneficiadas)

1 2 3 4 5

Apariencia (grado en el que el alimento parece atractivo, presentación) 1 2 3 4 5

Impacto medioambiental (efecto de la producción del alimento en el medio

ambiente)

1 2 3 4 5

177

Doctoral Thesis


En el siguiente apartado selecciona con una “X” la línea según la intensidad de cada

factor según corresponda.

-Si puedo ir a comer una hamburguesa en McDonald´s en el próximo mes, me

sentiría…

1. Emocionado

Nada __ __ __ __ __ __ __ Demasiado

2. Relajado

Nada __ __ __ __ __ __ __ Demasiado

3. Feliz

Nada __ __ __ __ __ __ __ Demasiado

4. Alegre

Nada __ __ __ __ __ __ __ Demasiado

5. Satisfecho

Nada __ __ __ __ __ __ __ Demasiado

6. Orgulloso

Nada __ __ __ __ __ __ __ Demasiado

178

Appendices

7. Seguro de sí mismo

Nada __ __ __ __ __ __ __ Demasiado

8. Aliviado

Nada __ __ __ __ __ __ __ Demasiado

9. Contento

Nada __ __ __ __ __ __ __ Demasiado

-Si NO puedo ir a comer una hamburguesa en McDonald´s en el próximo mes, me

sentiría…

10. Enfadado

Nada __ __ __ __ __ __ __ Demasiado

11. Frustrado

Nada __ __ __ __ __ __ __ Demasiado

12. Culpable

Nada __ __ __ __ __ __ __ Demasiado

13. Avergonzado

Nada __ __ __ __ __ __ __ Demasiado

179

Doctoral Thesis


14. Triste

Nada __ __ __ __ __ __ __ Demasiado

15. Deprimido

Nada __ __ __ __ __ __ __ Demasiado

16. Aburrido

Nada __ __ __ __ __ __ __ Demasiado

17. Inconfortable

Nada __ __ __ __ __ __ __ Demasiado

18. Ansioso

Nada __ __ __ __ __ __ __ Demasiado

19. Agitado

Nada __ __ __ __ __ __ __ Demasiado

20. Nervioso

Nada __ __ __ __ __ __ __ Demasiado

180

Appendices

En el apartado siguiente, por favor indica cuan tan fuerte estás de acuerdo o

desacuerdo con las siguientes afirmaciones de tu primera elección de fast food de

acuerdo a su producto principal. Evalúa cada ítem usando una escala de 5 puntos

donde: 1=Extremadamente desacuerdo, 2=Ligeramente desacuerdo, 3=neutral,

4=Ligeramente de acuerdo, y 5=Extremadamente de acuerdo.

Actitud a través del consumo de alimentos

5. Al comer la hamburguesa es placentero. (1) (2) (3) (4) (5)

6. Me gustaría divertirme comiendo la hamburguesa. (1) (2) (3) (4) (5)

7. Si como la hamburguesa sería satisfactorio para mí. (1) (2) (3) (4) (5)

8. Si como una hamburguesa es por el buen sabor que tiene. (1) (2) (3) (4) (5)

Actitud a través de la marca McDonald´s.

6. Me gusta la marca. (1) (2) (3) (4) (5)

7. Admiro a la marca. (1) (2) (3) (4) (5)

8. La marca encaja en mi vida. (1) (2) (3) (4) (5)

9. Me da vergüenza que me vean con la marca. (1) (2) (3) (4) (5)

10. Evito estar con la marca. (1) (2) (3) (4) (5)

Intención de compra en McDonald´s.

6. Es probable que compre productos de McDonald´s. (1) (2) (3) (4) (5)

7. Consideraría comprar el producto de McDonald´s si necesito un producto

de este tipo. (1) (2) (3) (4) (5)

8. Es posible comprar algún producto de McDonald´s. (1) (2) (3) (4) (5)

181

Doctoral Thesis


9. Compraría en McDonald´s la próxima vez. (1) (2) (3) (4) (5)

10. La probabilidad de que considere comprar en McDonald´s es alta. (1) (2)

(3) (4) (5)

Muchas gracias por su cooperación y participación

182

Identifying research topics in marketing sciencealong the past decade: a content analysis

Igor Barahona1 • Darıa Micaela Hernandez2 • Hector Hugo Perez-Villarreal3 •

Marıa del Pilar Martınez-Ruız4

Received: 20 January 2018 / Published online: 27 July 2018� Akademiai Kiado, Budapest, Hungary 2018

AbstractIn recent years, how marketing science is conceptualized has changed, as have the methods

through which data are investigated. This reconceptualization is making a significant

impact on the most important topics of this discipline. Here, a novel approach is used to

analyse a collection of 1169 abstracts from articles published in the Journal of Marketing

Research and the Journal of Marketing from 2005 to 2014. We apply statistical methods to

answer the following questions: How is vocabulary commonly used in marketing science?

What are the most relevant topics of these journals? Which articles are the most influ-

ential? What words do authors prefer? Is the consumer among the primary topics in

marketing research? A set of easy-to-read visual representations are provided to answer

these questions. We highlight two main findings: (i) consumers and customers are the main

topics of these marketing research journals, which emphasizes the growing interest in

consumers and consumer behaviour as the core of both brick-and-mortar and online

businesses; and (ii) in contrast to previous periods, product has become an essential

concept, perhaps due to the emergence of new product considerations and new and

enhanced interrelations.

Keywords Marketing � Content analysis � Keywords analysis � Multivariate statistics

Introduction

Considering that marketing science is constantly evolving, exploring feasible changes and

trends that might occur in the future is of strategic importance. Technology-enabled

marketing research comprises pertinent quantitative methods that allow for the retrieval of

& Igor [email protected]

1 Instituto de Matematicas, Universidad Nacional Autonoma de Mexico, Catedras CONACYT, Av.Universidad s/n. Col. Lomas Chamilpa Codigo, 62210 Cuernavaca, Morelos, Mexico

2 Universitat Politecnica de Catalunya - BarcelonaTech, Barcelona, Spain

3 Universidad Popular Autonoma del Estado de Puebla, Puebla, Mexico

4 Universidad de Castilla - La Mancha, Ciudad Real, Spain

123

Scientometrics (2018) 117:293–312https://doi.org/10.1007/s11192-018-2851-2(0123456789().,-volV)(0123456789().,-volV)

183

Appendice 3.Publications

coherent sequential information from massive datasets in a rapid and accurate manner

(Wang et al. 2014). In this sense, it is also relevant to investigate the triggers that create

breakthroughs in the evolution of this discipline (Kumar 2015). Within marketing research,

a key idea is investigating differences among topics, an idea that has been of interest to the

most prestigious marketing journals in the last decade. These kinds of studies typically use

content analysis and text mining. In a study by Huber et al. (2014) that was published in a

special 50th anniversary issue of the Journal of Marketing Research (JMR), the authors

clustered main topics according to each editor’s tenure. Later, the topics preferred by each

editor were identified by calculating a correspondence analysis (CA). Similarly, Kolbe and

Burnett (1991) reviewed 128 studies that used different kinds of content analysis as their

primary method. Their findings suggested coefficients of reliability for content-analysis

methods. In Morris (1994), the author performed a comparison between computerized and

human outputs, and his results showed that computerized content-analysis tends to be more

reliable and stable.

If these methodologies are applied to conducting a literature review, a common factor

arises: All of these methods are capable of disclosing topics and key concepts on which

researchers are focusing. Additionally, the relevance of these types of studies is enhanced

if they are drawn from the most prestigious marketing science journals, namely the Journal

of Marketing (JM) and the Journal of Marketing Research (JMR). We also consider three

important academic indexes: Scopus, Thomson-Reuters or Web of Science (WoS), and ISI.

Scopus has more indexed publications than ISI (Leydesdorff et al. 2010). However, ISI is

considered more prestigious in the social sciences. According to SCImago (2017), the JM

is the top journal in the marketing industry; the JMR is third. As pioneering publications,

these journals represent the trajectory of the discipline. Currently, they are the official

media of the American Marketing Association (AMA).

In addition to being official media for the AMA, these journals are focused on

demonstrating new techniques for tackling marketing challenges, and thus can be con-

sidered a strong link between theory and practice. According to Thomson-Reuters indexes,

in 2016 the JM had an impact factor of 5.318 and the JMR had a 3.654 impact factor

(Thomson Reuters 2016a, b). A complementary criterion for evaluating these journals is

their impact factor performance during the last decade (2005–2014). Both publications

should be included on the Journal Citations Reports (JCR) for the aforementioned period,

as shown in the following table.

Considering the information presented Table 1, it is clear that in the marketing area the

JM has been consistently strong over time. On the other hand, although the JMR did not

achieve the highest impact factors for 2013–2014, it earned higher scores from 2007 to

2014. Their respective impact factor scores were considered as a criterion for selecting

these two journals for the present study.

Furthermore, the JM, which has a long tradition in marketing (we highlight that the first

issue of this journal was published in 1936) and has some of the greatest scientific rele-

vance, recently published a similar study. In this work, Kumar (2015) discussed the

evolution of marketing science by investigating its ‘triggers.’ The author also proposes

future lines of research and predominant metaphors in the field. Using Kerin (1996) cat-

egorization as a starting point, a new perspective on marketing science is drawn. By

investigating how the topics published in the JMR have evolved as well as by identifying

their corresponding triggers and the scope of the covered topics, the contributing factors

are discussed. A trigger is the influence of academics who introduce new knowledge in

response to practitioners’ concerns. These factors influence the way marketing science will

be shaped in the future.

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Given this framework for marketing science and bibliometric studies over the last two

decades, the general objective of this paper is to investigate the two most important

journals in the marketing area: the JM and the JMR. This paper is structured in five

sections. First, a literature review, which discusses applications of the techniques proposed

herein, is provided. Then, the methodology is introduced in section three. Section four

contains the obtained results. Discussion and limitations of this research are presented on

the last section.

Literature review

The historic evolution of marketing science between 1936 and 1945 was accurately drawn

by Kerin (1996), who proposed the prominent topic ‘illuminating marketing principles and

concepts’ as a starting point, as well as the metaphor ‘marketing as applied economics,’

and its triggers ‘understanding of marketing principles through case studies,’ ‘need to

comprehend government legislation and trade regulations,’ and ‘marketing research topics

and implications for marketing practice.’ For the most recent period, 2013 and onward, the

most prominent topic is ‘marketing at the core and influence of new media.’ Similarly, the

related metaphor for this period is ‘marketing as an integral part of the organization,’ and

the triggers are ‘changes in media usage patterns,’ ‘focus on marketing efficiency and

effectiveness,’ and ‘value generated by engaging stakeholders of the firm.’ Moreover,

Huber et al.’s (2014) study ‘A topical history of the JMR’ also warrants attention. The way

topics and contents evolved during a 50-year period (1964–2012) is discussed. Huber et al.

(2014) also identify how this journal gradually increased its emphasis on marketing

research methods and advertising, and also expanded its coverage to other substantive

Table 1 Impact factor JCR marketing category

Journal 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Journal of Marketing 4.132 4.831 3.75 3.598 3.779 3.77 5.472 3.368 3.819 3.938

Journal of Supply ChainManagement

0 0 0 0 0 5.853 2.65 3.32 3.717 3.857

Journal of MarketingResearch

2.611 2.389 1.739 2.574 3.099 2.8 2.517 2.254 2.66 2.256

Marketing Science 3.788 3.977 3.964 3.309 2.194 1.724 2.36 2.201 2.208 1.86

Journal of ConsumerResearch

2.161 2.043 1.738 1.592 3.021 2.59 3.101 3.542 2.783 3.125

Journal of the Academyof Marketing Science

1.485 1.463 1.18 1.289 1.578 3.269 2.671 2.57 3.41 3.818

Journal of PublicAdministrationResearch and Theory

1.451 1.655 1.982 1.509 1.776 2.086 2.176 1.951 2.875 2.833

Academy ofManagementPerspectives

0 0 0.594 1.118 1.405 2.47 3.75 3.174 2.826 3.354

International Journal ofResearch in Marketing

1.222 1.28 1.071 1.611 1.873 1.365 1.662 1.781 1.71 1.575

Journal of Retailing 0.894 1.196 2.054 4.095 4.567 2.257 2.75 1.152 1.193 1.754

Source: Own elaboration with 2016 Journal Citation Reports� (Thomson Reuters 2016a, b)

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topics, such as consumer behaviour and social networks. Based on this analysis, it can be

inferred that the editorial style of the journal moved from ‘evolutionary’ to ‘revolutionary.’

The study concluded that during the investigated period, the most common topic based on

the number of published articles was ‘consumer behaviour.’

Since 1990, the emergence of more powerful computers prompted the proliferation of

two of the most important methods for retrieval data: text mining (TM) and content

analysis (CAN). According to Stavrianou et al. (2007), TM focuses on analysing textual

data ‘so that, new previously unknown knowledge is discovered.’ By comparison, CAN

attempts to compress large volumes of words and texts into fewer categories by a given set

of coding rules. TM and CAN both aim to extract common themes and threads by counting

words. Although both can use computer algorithms, TM has the capacity to process natural

languages. Meanwhile, CAN is a systemic and replicable technique, which makes it

possible to synthesize a large number of words into smaller sets of categories (Stemler

2001). For instance, Stemler et al. (2011) conducted a content analysis of school mission

statements to identify their primary stated reasons for existence, detect shifts in public

opinion with respect to the passing of time and recognize those schools that introduce key

concepts. Weismayer and Pezenka (2017) investigated keywords in articles published by

International Marketing Review (IMR) from 1988 to 2016 and ENTER conference pro-

ceedings from 2005 to 2016. Their goal was to identify relevant topics in different research

areas and predict trends on published articles. Weismayer and Pezenka (2017) suggested

that CAN is the most valid way to determine editor/reviewer predilections. Fang et al.

(2017) conducted a bibliometric study with a five-step methodology using 105 published

articles related to electronic commerce (e-commerce). The study provided evidence of the

suitability of methods such as TM and CAN for performing literature reviews and bib-

liometric studies. Nel et al. (2011) conducted a content analysis of 407 papers published by

the Journal of Services Marketing during 1998–2008 and showed trends in research topics.

Similarly, Glaser et al. (2017) and Munoz-Leiva et al. (2012) found that the number of

bibliometric studies, which apply either TM or CAN, increased.

Methodology

A five-step methodology was implemented to address our research objectives. First, how

data were collected is described, followed by an explanation of the properties of the

dataset. The third step introduces the statistical methods, and details of how the charac-

teristic words are identified is provided in step four. The software is presented in the final

step (see Fig. 1).

Data collection

Over the years, marketing science has changed in terms of its focus, emphasis, and pri-

orities. In this regard, the JMR and the JM have been forerunners introducing these

changes, thus garnering the attention of academics, businesspeople, and practitioners. A

collection of 1169 abstracts, which cover the period from 2005 to 2014, were obtained

from the websites of JMR and JM. As additional measures of standardization, all abstracts

included the title, name of the first author, country, university, and year of publication.

Figure 2 is a classification of the documents based on country. Similarly, Fig. 3 classifies

the same group of abstracts according to the year of publication.

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As shown in Fig. 2, about 70% of the articles published between 2005 and 2014 were

submitted by U.S. authors. In second place, the Netherlands accounted for 6.5% of the

publications; Canada was in third place with 4.4% of the articles published. Researchers

from these three countries represent 81% of the all papers published by both journals. The

remaining 19% is distributed among 26 different countries.

Fig. 1 Five-step methodologyapplied to this research

821

76 52 50 22 19 19 16 14 13 11 10 8 7 5 4 3 3 2 2 2 2 2 1 1 1 1 1 1

Fig. 2 Published articles in JMR and JM by country, from 2005 to 2014

105 102 108 110134 140

156

12099

143

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Fig. 3 Published articles in JMR and JM by year, from 2005 to 2014

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With respect to the year of publication, the highest number of articles (n = 156) was

published in 2011. In contrast, 2013 was the year with the lowest number, with 99 pub-

lished articles. In short, between 2005 and 2014, both journals published an average of 116

articles per year, with a standard deviation of 28.1. In Tables 2 and 3 information related

with published articles by JM and JMR is provided in a more detailed way.

Properties of the dataset

The body under analysis includes 1169 documents and 120,340 terms. On average, each

abstract contains 103 terms. Regarding the total number of words, the total text analysed

has 185,437 words, which is equal to 158 words for each abstract. This last measure is

relevant because it documents the usual length of abstracts, which is used by researchers

who publish in these journals.

Table 4 shows the percentage of unique terms. This number refers to words that appear

at least one time in the text regardless of their frequency (a catalogue of words). The total

number of words is obtained by counting all in the document. While the whole dataset

contains 7.4% unique terms, the mean per abstract is 70.4%. The low percentage of unique

Table 2 JMR articles by Issue and year


2005 42 (1): 13 articles, 42 (2): 14 articles,42 (3): 15 articles, 42 (4): 16 articles

Dick R. Wittink (2003–2005)Russell S. Winer (2005–2006)


Russell S. Winer (2005–2006)Joel Huber (2006–2009)


Joel Huber(2006–2009)

2008 45 (1): 9 articles, 45 (2): 9 articles,45 (3): 10 articles, 45 (4): 9 articles,45 (5): 9 articles, 45 (6): 10 articles

Joel Huber(2006–2009)


Joel Huber (2006–2009)Tulim Erden (2009–2012)


Tulim Erden (2009–2012)

2011 48 (1): 15 articles, 48 (2): 15 articles,48 (3): 15 articles, 48 (4): 11 articles,48 (5): 10 articles, 48 (Supplement 1): 15 articles48 (6): 11 articles

Tulim Erden (2009–2012)


Tulim Erden (2009–2012)Robert Meyer (2012–2016)


Robert Meyer (2012–2016)

2014 51 (1): 21 articles, 51 (2): 8 articles,51 (3): 8 articles, 51 (4): 11 articles,51 (5): 7 articles

Robert Meyer (2012–2016)

Source: Own elaboration

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terms is a measure of the vocabulary consistency. The percentage is inversely related to the

uniformity of the vocabulary of a given document. With this regard, Becue-Bertaut (2014)

suggested that percentages closer to 1.0 indicate a high diversity of vocabulary. In this

case, it can be inferred that the whole dataset is uniform in terms of vocabulary use. This

makes sense, given that all the abstracts were published in journals of the same field, and

therefore have similar features.

Table 3 JM articles by Issue and year


2005 69 (1): 9 articles, 69 (2): 9 articles69 (3): 10 articles, 69 (Special Section): 11 articles69 (4): 8 articles

Ruth N. Bolton (2002–2005)Roland T. Rust (2005–2008)

2006 70 (1): 10 articles, 70 (2): 10 articles70 (3): 10 articles, 70 (4): 12 articles

Roland T. Rust (2005–2008)

2007 71 (1): 13 articles, 71 (2): 13 articles71 (3): 12 articles, 71 (4): 12 articles

Roland T. Rust (2005–2008)

2008 72 (1): 9 articles, 72 (2): 9 articles72 (3): 9 articles, 72 (4): 9 articles72 (5): 9 articles, 72 (6): 9 articles

Roland T. Rust (2005–2008)Ajay K. Kohli (2008–2011)

2009 73 (Special Section): 9 articles73 (1): 9 articles 73 (2): 9 articles73 (3): 8 articles 73 (4): 8 articles73 (5): 8 articles 73 (6): 18 articles

Ajay K. Kohli (2008–2011)



2011 75 (1): 8 articles, 75 (2): 8 articles75 (3): 8 articles, 75 (4): 15 articles75 (5): 8 articles, 75 (Supplement 1): 9 articles75 (6): 8 articles



Gary L. Frazier (2011–2014)



2014 78 (1): 8 articles, 78 (2): 8 articles78 (3): 8 articles, 78 (4): 8 articles78 (5): 8 articles



Table 4 Descriptive statistics ofthe dataset under analysis

Descriptive statistics Abstract mean Total

Number of terms 103.0 120,340.0

Number of unique terms 71.0 8874.0

Percent of unique terms 70.4% 7.4%

Number of words 158.6 185,437.0

Average word length 5.9 5.9

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The two most common techniques used for information retrieval are lemmatization and

stemming. Bartol and Stopar (2015) described the first as the methods for removing

inflectional endings on words; Meyer et al. (2008) explained stemming as the algorithms

used for removing word suffixes while preserving their radical. One advantage of

lemmatization is that it first uses glossaries to ensure words are properly grouped. A

limitation observed in our work is that stemming was carried out manually, and thus is

extremely time consuming. Therefore, we suggest the use of glossaries (based on the

lemmatization approach) for future research to reduce time spent on repetitive manual

tasks.

Prior to calculating the basic descriptive statistics, the dataset was prepared. Preposi-

tions, conjunctions, personal pronouns, articles, and demonstratives were removed.

Although the stop-words proposed in the R package ‘tm’ (Feinerer 2017) were used as a

reference, the stemming procedures were implemented manually. The central idea is to

reduce text’s complexity without severe loss or distortion of information. The algorithm

proposed by Porter (1997), which has been proven to provide accurate results for stemming

texts in English in a variety of disciplines, was taken as guideline. Using this approach,

corresponding equivalences were obtained; that is, words with the same meaning and

words that appeared in singular and plural were grouped as one word. For example, the

words ‘accountability,’ ‘accountable,’ and ‘accounted’ should be treated as ‘account’; the

words ‘branding’ and ‘brands’ should be treated as ‘brand.’ With the purpose of creating

graphical representations, minimum thresholds were imposed. Only words with frequen-

cies equal to 20 and higher were retained. Similarly, abstracts using a given word 15 times

or more, were also kept. As a result, 994 of the 8874 different words and 80,123 of the

185,437 occurrences were kept. The yielded document text matrix (dtm) is of order

994 9 1164. The rows are related to the abstracts and the columns are related to the words.

In addition, there are three categorical variables in the dataset that relate to year of

publication, author name, and institution. These categorical variables were incorporated for

the last part of the analysis.

Multivariate methods (CA and MFACT)

According to Benzecri (1979), Murtagh (2005), Barahona (2016) and Becue-Bertaut

(2014), CA is widely used in the field of text mining. The most remarkable feature of CA in

the context of a literature review is its capacity for plotting abstracts and words in such a

way that hidden relationships are uncovered. For example, similarities and differences

among abstracts, in terms of the vocabulary used, are identified. Below is a list of outputs

obtained through the CA.

• Identifying similarities between abstracts, given their verbal contents.

• Detecting similar words, based on their distribution.

• Making associations about similar words, given the context in which the words were

used.

• Providing visual representations of abstracts and words.

Bansard et al. (2006) stated that words frequently used in the same abstract are all

together building a topic and they are considered to belong to the same metakey. It is

important to note that one word can belong to one or more metakeys (indeed, this is very

frequent). This scenario indicates that the same word can be used in several contexts, each

of which might have a different meaning. For instance, the word ‘environment’ may be

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related to the quality levels of air or water, but in another context, ‘environment’ could

mean conditions and settings in the workplace. Finally, CA is capable of quantitatively

associating a given metadoc with a metakey that together characterize the same axis. In this

case, it is inferred that abstracts belonging to a same metadoc are using words, which in

turn are associated on the same metakey. If this lexical table is complemented with the

categorical year of publication, then the analysis changes into a Multiple Factor Analysis

of Contingency Tables (MFACT). A detailed explanation of metakey and metadoc con-

cepts, as well as the results obtained through both methodologies (CA and MFACT) and

their graphical representations, are provided in Sect. 4.2.

Types of results

The application of the correspondence analysis and its variants makes the inclusion of

categorical variables possible. This allows us to obtain two types of results, as follows:

• The first approach comprises results that are commonly obtained through CA: namely

eigenvalues, representations of row-abstracts and column-words, and distances

between abstracts based on both Euclidean and Chi-squared distances. While the

former is given by the squared sum of differences, the last includes a constant

adjustment that is calculated in terms of each column-row profile. The distributional

equivalence, which is a property of a traditional CA, allows for merging two or more

column-profiles that have the same relative values without affecting distance between

row-profiles.

• Second, an edited version of the original table is yielded by linking each row-abstract

with the year of publication. The result is a table of quantitative and categorical

variables. The MFACT is a suitable tool for dealing with mixed data tables (Kostov

et al. 2015). MFACT balances the groups’ effect (given by year of publication) on the

first dimension by dividing the columns-words profiles of each group by the first

eigenvalue. Then, the highest inertia of each group is standardized to 1. Interpretation

for the MFACT remains identical to the classical CA. Graphical representations based

on the MFACT allow us to compare typologies of each group in a reduced dimensional

space with the purpose of evaluating extent to which positions of row-abstracts are

similar from one group to another.

Characteristic words and abstracts

With the purpose of providing quantitative indicators of the most frequent terms in the

dataset, modelling a hypergeometric distribution (HD) is proposed. HD is a discrete

probability distribution, which defines the probability of achieving k successes in n at-

tempts, without replacement. Assuming N is a finite population that contains K successes,

the following notation is proposed:

– n::; The total number of words-occurrences in the whole dataset;

– n;j;, The number of words-occurrences in part j;

– ni::; The total count of the word i in the whole corpus;

– nij The count of the word i in part j.

The total frequency nij of word i in part j is contrasted with other sums. These sums are

obtained with all possible samples composed of nj occurrences randomly extracted from

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the whole dataset without replacement. If word i is relatively more frequent in part j than in

the whole sample, that is: nij=nj\ni=n::, then the p value is calculated as stated in formulas

(1) and (2).

pi;j ¼Xn:jx¼nij

ni:x

� �n:: � ni:n:j � x

� �

n::n:j

� � ð1Þ

pi;j ¼Xnijx¼1

ni:x

� �n:: � ni:n:j � x

� �

n::n:j

� � ð2Þ

Based on formulas (1) and (2), a hypothesis test (one-tail) is conducted to assess the

significance of the first eigenvalue, and, consequently, to establish a quantitative link

between chronological evolution and the use of vocabulary. The null hypothesis states: A

chronological dimension of the vocabulary does not exist, and, hence, tested words are

exchangeable across the variable year of publication. Randomly, the variable year column

is permuted in the lexical table without replacement, and a p value is calculated on every

permutation. An empirical distribution for the first eigenvalue (under Ho) is obtained by

repeating this procedure many times as a number nears n::;. The algorithms proposed by

Becue-Bertaut (2014) and Lebart et al. (1997) are taken as a guideline for these purposes. It

is important to conduct a large number of permutations to compute the p value as accu-

rately as possible.

Statistical software

The main reasons for using the software R version 3.3.3 (2017-03-06) ‘Another Canoe’ in

this study are detailed below. First, it is open source software, which allowed us to use it at

different locations without licence restrictions. Moreover, considering that R is a collab-

orative project, it allowed us to maintain contact with some authors of the libraries that

were used during our calculations. Libraries and functions written under the R environment

are constantly up to date, which ensured that state-of-the-art computational algorithms

were used in our analysis. Specifically, the function BiblioMineR (Hernandez Ramırez

2012) and the packages CA (Greenacre et al. 2017), RcmdrPlugin.temis (Bouchet-Valat

and Bastin 2013) and FactoMineR (Le et al. 2008), among others, were utilized.

Results

Glossary of most frequent terms

The first analysis of the glossary of most frequent terms allowed us to conclude that this is

a repetitive corpus. Note that only 25 words represent 24% of the occurrences in the whole

dataset, which is equal to 28,881. ‘Consumers’ was the most frequent word with 1527

occurrences, which means that it appears in 47% of the abstracts. ‘Product’ was second

(1450 occurrences), followed by ‘customer’ (1269 occurrences), appearing in 36 and 24%

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of the abstracts, respectively. These three terms with ‘effect’ (1239 occurrences), ‘brand’

(1160 occurrences), ‘marketing’ (1077 occurrences), and ‘firm’ (1019) shape the main

content of both journals. We found that nearly 28% of the abstracts include all of these

words together. This overall perspective allows us to take a first approach in identifying

what seems to be of interest to JMR and JM authors. Their efforts are directed toward

discussing effects on products, consumers, and brands through ‘study’ (880), ‘model’

(748), ‘market’ (691), ‘research’ (659), and ‘price’ (617).

These findings yield supporting evidence that consumer behaviour was one of the most

relevant topics during the investigated decade. The terms ‘consumers’ and ‘customer’ are

among the top ten recurrences for the whole dataset. Moreover, these results are similar to

those obtained by Huber, Kamakura, and Mela (2014), which highlighted how the JMR

gave increasing importance to the topic of consumer behaviour during the investigated

period. Moreover, conclusions obtained by Huber, Kamakura, and Mela (2014) in relation

to the term ‘product’ also drew our attention. Consistent with these results, they ranked

‘product’ at position nine of prevalence in abstracts for 1964–2012. It appears in second

place of the rankings in the current study (see Table 5). Considering this, it is inferred that

the concept of ‘product’ gained more attention in the last decade in contrast to previous

periods (1964–2001).

Table 5 List of the 25 most fre-quent terms

Word Glossary frequency No. documents

Consumer 1527 554

Product 1450 417

Customer 1269 284

Effect 1239 574

Brand 1160 228

Marketing 1077 442

Firm 1019 336

Study 880 518

Use 786 537

Model 748 360

Market 691 282

Research 659 462

Price 617 173

Data 577 394

Relationship 522 222

Value 509 203

Sale 495 169

Decision 456 236

Performance 455 193

Choice 440 183

Level 426 253

Show 398 334

Behaviour 386 222

Find 383 302


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Most relevant topics and its related abstracts

Correspondence analysis is a multivariate statistical technique that is applied to categorial

data and provides means for summarizing large datasets on a reduced dimensional space.

In this context, CA is applied to identify those ‘metakeys’ and ‘metadocs,’ which better

describe similarities among abstracts based on the words they use. It is important to clarify

that a metakey is related to a given word used in one or more abstracts, whereas a metadoc

is related to an abstract. In this way, two or more metadocs might be related in function to

the same metakeys. Researchers might identify the set of words (metakey ?/metakey-) that

most contribute to the inertia and lie on the positive/negative part of the axis. Simulta-

neously, the set of documents that most contribute to the inertia (metadoc ?/metadoc-) and

lie on its positive/negative part might also be identified.

For the purpose of creating intuitive visualizations, only those metakeys and metadocs

with strong presence on the principal axes were considered. Abstracts using a given word

15 times or more were kept. Words with frequencies equal to 20 and higher were also

retained. According Lebart et al. (1997), this improves the comprehension of associations

among words and abstracts. The first five components, obtained through the correspon-

dence analysis, were retained. From this group, the pair with the highest eigenvalues was

taken as axes of the charts provided below. While the eigenvalue for the first axis is equal

to 0.25, its value for the second axis is 0.21. These two axes are able to accurately describe

the emergence of the most relevant words of the investigated dataset, taking into account

that they also have the biggest eigenvalues. Note that previously mentioned rules apply

only to visual representations (Fig. 4). Additional criterion, which consisted of retaining

Fig. 4 Most contributory abstracts/words (CA)

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only those words and abstracts with a contribution three times higher than the mean

(average), was applied for elements listed in Table 6.

With regard to Fig. 4, note the positive part of the first axis, which is also called

(DIM1?), which notes a set of words (metakey?) that is closely related with a metadoc? .

The words ‘consumer,’ ‘choice,’ ‘price,’ ‘consumption,’ ‘preference,’ and ‘self’ are

highlighted in this area. We also identify the negative section of the first axis (D1M1-),

where the metakey- is located (consumer relationship). It is composed of the words

‘customer,’ ‘firm,’ ‘marketing,’ ‘relationship,’ ‘performance,’ ‘business,’ and ‘market.’ At

the same time, the mentioned words are closely related to the metadoc-, which is composed

of the articles ‘363,’ ‘715,’ ‘731,’ and others.

Similarly, metakey2 ? is distinguished by the topic ‘Developing strategies and pro-

grams for pricing.’ Note that it is located in the positive part of the second axis (D1M2?),

and it is composed of the words ‘price,’ ‘retailer,’ ‘store,’ ‘model,’ ‘search,’ and ‘pricing.’

Note that articles ‘92,’ ‘77,’ and ‘277’ compose metadoc2? . With respect to the negative

part of (D1M2-), ‘emotional marketing’ is identified as the most remarkable topic. The

articles that feature this topic are ‘118,’ ‘309,’ ‘1045,’ and ‘726.’

While the most contributing metakeys (words) are related to a given topic and also

introduced in Table 6, the way abstracts and words were aggregated in respect to the year

of publication is presented in Table 7. The criterion for selecting words and abstracts was

their contribution to the total inertia. In Table 6, those contributions higher than three times

the mean (average) of the total inertia were kept. In Table 7, elements equal or higher than

the mean of the total inertia are presented. In both cases, axes with the biggest eigenvalues

are used as references.

Chronological evolution

To investigate the chronological evolution of the vocabulary, the abstract-words matrix

was transformed into a mixed table by adding the variable year of publication as a cate-

gorical variable. Consequently, the CA turned out to be a MFACT. This makes it possible

to identify similarities and differences in vocabulary over time. Periods characterized by

specific terms and important variations in the use of the vocabulary were also identified. By

conducting this analysis, we can answer questions such as: Which groups of documents,

given a year of publication, are similar or different? Which periods are characterized by the

introduction of new vocabulary? How has vocabulary evolved over time?

The input for the MFACT consisted of a mixed table on which the categorical variable

year of publication is distributed on rows. Columns are reserved for words. In this form,

our matrix contains 10 rows (years) and 994 columns (words). The eigenvalues for the first

five components (obtained from the MFACT) are presented in Table 8. Note that the

eigenvalues are, in general, smaller than those obtained through the traditional corre-

spondence analysis. Typical structures on mixed tables are among the main causes of the

small eigenvalues. These properties were exhaustively studied by Kostov et al. (2015),

Greenacre et al. (2017) and Lebart et al. (1997) among others. While the eigenvalue for the

first component is 0.032, the value for the second is 0.026. The same rules previously

applied to the CA are repeated for the MFACT: retain five axes in the initial calculation

and select the two with the biggest eigenvalues. Finally, the projection of words with a

contribution of three times higher than the mean (average) was carried out. Therefore, it

ensured that the most representative words were visualized, either due to the biggest

eigenvalues on the axes or the high contribution of the chosen words.

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Table 6 Main topics

DIM TOPICS Metakeys

DIM1?

Consumer Choice ‘‘consumer’’ ‘‘choice’’ ‘‘price’’ ‘‘consumption’’‘‘preference’’ ‘‘self’’ ‘‘people’’ ‘‘option’’ ‘‘attribute’’‘‘food’’ ‘‘product’’ ‘‘hedonic’’ ‘‘brand’’ ‘‘evaluation’’‘‘goal’’ ‘‘extension’’ ‘‘experiment’’ ‘‘search’’ ‘‘health’’‘‘purchase’’ ‘‘less’’ ‘‘versus’’

DIM1-

Customer Relationship Management ‘‘customer’’ ‘‘firm’’ ‘‘marketing’’ ‘‘relationship’’‘‘performance’’ ‘‘business’’ ‘‘market’’ ‘‘satisfaction’’‘‘supplier’’ ‘‘value’’ ‘‘orientation’’ ‘‘employee’’ ‘‘return’’‘‘service’’ ‘‘stock’’ ‘‘capability’’ ‘‘financial’’‘‘shareholder’’ ‘‘management’’ ‘‘innovation’’ ‘‘ties’’‘‘organizational’’ ‘‘portfolio’’ ‘‘relational’’ ‘‘risk’’‘‘equity’’ ‘‘trust’’ ‘‘metric’’ ‘‘governance’’ ‘‘frontline’’‘‘salesperson’’ ‘‘development’’ ‘‘knowledge’’ ‘‘manager’’‘‘loyalty’’ ‘‘network’’ ‘‘strategic’’ ‘‘retention’’

DIM2?

Developing strategies and programsfor pricing

‘‘price’’ ‘‘retailer’’ ‘‘store’’ ‘‘model’’ ‘‘search’’ ‘‘pricing’’‘‘manufacturer’’ ‘‘demand’’ ‘‘data’’ ‘‘household’’‘‘advertising’’ ‘‘endogeneity’’ ‘‘category’’ ‘‘elasticity’’‘‘distribution’’ ‘‘retail’’ ‘‘method’’ ‘‘promotion’’‘‘elasticities’’ ‘‘channel’’ ‘‘private’’ ‘‘parameter’’‘‘heterogeneity’’ ‘‘estimates’’ ‘‘grocery’’ ‘‘market’’‘‘channels’’ ‘‘share’’ ‘‘unobserved’’ ‘‘sale’’ ‘‘estimation’’‘‘shopping’’ ‘‘estimate’’ ‘‘label’’ ‘‘competitive’’‘‘quantity’’ ‘‘optimal’’ ‘‘competition’’ ‘‘profit’’

DIM2-

Emotional Marketing ‘‘self’’ ‘‘emotion’’ ‘‘employee’’ ‘‘emotional’’ ‘‘evaluation’’‘‘goal’’ ‘‘message’’ ‘‘regulatory’’ ‘‘brand’’ ‘‘extension’’‘‘hedonic’’ ‘‘people’’ ‘‘fit’’ ‘‘experience’’ ‘‘corporate’’‘‘influence’’ ‘‘personality’’ ‘‘knowledge’’ ‘‘frontline’’‘‘utilitarian’’ ‘‘identity’’ ‘‘process’’ ‘‘positive’’‘‘participation’’ ‘‘attitude’’ ‘‘study’’ ‘‘consumption’’‘‘engagement’’ ‘‘role’’ ‘‘processing’’ ‘‘versus’’ ‘‘focus’’‘‘service’’ ‘‘negative’’

DIM3?

Design and management ofintegrated marketing channels

‘‘supplier’’ ‘‘price’’ ‘‘goal’’ ‘‘customer’’ ‘‘service’’‘‘relationship’’ ‘‘consumption’’ ‘‘pricing’’ ‘‘saving’’‘‘food’’ ‘‘trade’’ ‘‘decision’’ ‘‘employee’’ ‘‘people’’‘‘business’’ ‘‘buyer’’ ‘‘aversion’’ ‘‘option’’ ‘‘salesperson’’‘‘choice’’ ‘‘ties’’ ‘‘seller’’ ‘‘outcome’’ ‘‘reference’’‘‘retailer’’ ‘‘performance’’ ‘‘orientation’’ ‘‘hedonic’’‘‘manufacturer’’

DIM3-

Brand Equity ‘‘brand’’ ‘‘extension’’ ‘‘personality’’ ‘‘association’’‘‘advertising’’ ‘‘branding’’ ‘‘equity’’ ‘‘fit’’ ‘‘branded’’‘‘category’’ ‘‘success’’ ‘‘stock’’ ‘‘value’’ ‘‘metric’’‘‘return’’ ‘‘similarity’’ ‘‘risk’’ ‘‘measure’’ ‘‘image’’‘‘shareholder’’ ‘‘measures’’ ‘‘attitude’’

DIM4?

Design and management ofintegrated marketingcommunications

‘‘marketing’’ ‘‘advertising’’ ‘‘method’’ ‘‘media’’ ‘‘choice’’‘‘design’’ ‘‘model’’ ‘‘stock’’ ‘‘search’’ ‘‘review’’‘‘conjoint’’ ‘‘attribute’’ ‘‘recommendations’’ ‘‘approach’’‘‘traditional’’ ‘‘complexity’’ ‘‘network’’ ‘‘investor’’‘‘rating’’ ‘‘web’’ ‘‘advertisement’’ ‘‘site’’ ‘‘emotion’’‘‘option’’ ‘‘heterogeneity’’ ‘‘metric’’ ‘‘firm’’‘‘respondents’’ ‘‘content’’ ‘‘response’’ ‘‘activity’’‘‘preference’’ ‘‘decision’’ ‘‘researcher’’ ‘‘social’’

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Table 6 continued

DIM TOPICS Metakeys

DIM4-

Marketing Channels ‘‘price’’ ‘‘brand’’ ‘‘extension’’ ‘‘retailer’’ ‘‘store’’‘‘manufacturer’’ ‘‘private’’ ‘‘supplier’’ ‘‘label’’ ‘‘image’’‘‘category’’ ‘‘retail’’ ‘‘pricing’’ ‘‘employee’’ ‘‘loyalty’’‘‘labels’’ ‘‘shopping’’ ‘‘promotion’’ ‘‘reference’’‘‘grocery’’ ‘‘share’’ ‘‘personality’’ ‘‘national’’ ‘‘success’’‘‘frontline’’ ‘‘service’’ ‘‘identification’’ ‘‘discounts’’‘‘business’’ ‘‘evaluation’’ ‘‘buying’’

DIM5?

Value Networks ‘‘supplier’’ ‘‘extension’’ ‘‘governance’’ ‘‘method’’ ‘‘trust’’‘‘model’’ ‘‘relationship’’ ‘‘conjoint’’ ‘‘knowledge’’‘‘attribute’’ ‘‘partner’’ ‘‘design’’ ‘‘ties’’ ‘‘choice’’‘‘network’’ ‘‘measurement’’ ‘‘parameter’’ ‘‘approach’’‘‘brand’’ ‘‘performance’’ ‘‘selection’’ ‘‘proposed’’‘‘relational’’ ‘‘decision’’ ‘‘innovation’’ ‘‘organizational’’‘‘predictive’’ ‘‘unobserved’’ ‘‘incentive’’ ‘‘preference’’‘‘approaches’’ ‘‘validity’’ ‘‘distribution’’

DIM5-

Marketing Metrics ‘‘stock’’ ‘‘advertising’’ ‘‘return’’ ‘‘emotion’’ ‘‘risk’’‘‘investor’’ ‘‘price’’ ‘‘spending’’ ‘‘shareholder’’‘‘satisfaction’’ ‘‘negative’’ ‘‘finance’’ ‘‘financial’’‘‘message’’ ‘‘impact’’ ‘‘review’’ ‘‘systematic’’ ‘‘food’’‘‘loss’’ ‘‘firm’’ ‘‘long’’ ‘‘promotion’’ ‘‘equity’’ ‘‘store’’‘‘term’’ ‘‘health’’ ‘‘value’’ ‘‘consumption’’ ‘‘cash’’‘‘positive’’ ‘‘abnormal’’ ‘‘metric’’ ‘‘short’’ ‘‘search’’‘‘emotional’’ ‘‘online’’ ‘‘expenditures’’ ‘‘net’’ ‘‘customer’’


Table 7 Distribution of abstracts/words

Aggregation of abstracts and words according to the categorical variable year

Years Abstracts Occurrences before Occurrences after Mean length Words before Words after

2005 105 12,251 6122 116.68 2507 881

2006 102 13,285 6675 130.25 2653 912

2007 108 15,577 7903 144.23 2849 929

2008 110 16,497 8402 149.97 2912 939

2009 134 19,516 9827 145.64 3207 957

2010 140 19,286 9415 138.75 3321 965

2011 156 21,729 10,558 139.29 3466 958

2012 120 17,381 8855 144.84 3069 947

2013 51 6734 3304 132.04 1818 752

2014 143 19,377 9596 135.50 3269 935

Overall 1169 161,633 80,657 138.27 8800 994


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197

How has the vocabulary evolved over time?

A type of big picture of how the vocabulary had evolved over the years is shown in Fig. 5.

There are three important periods where the vocabulary shifted: 2005–2006 in blue,

2007–2009 in grey, and 2010–2014 in green. For the horizontal axis, while words related to

‘customer satisfaction’ and ‘market model’ are displayed on the negative part of the axis,

words referring to ‘social networks’ and ‘mobile technologies’ are projected in the positive

area. With respect to the vertical axis, the positive area is characterized by the words

‘regulatory,’ ‘fit,’ and ‘retailer.’ On the negative part, the words ‘brand,’ ‘networks,’

‘demonstrate,’ and ‘stock’ are found.

In the first period, from 2005 to 2006, authors published in the journals were mainly

writing about regulatory issues, emotional shopping, and fitting models. During the second

period (2007–2009), authors focused their attention on the customer’s satisfaction, loyalty,

and trust. Topics such as market models, branding, firm returns, and stocks are charac-

teristic of this period. Finally, in the third period, which comprises 2010–2014, topics such

as social networks and contents, mobile technologies, online shoppers, reviews, and

demonstrations emerged.

Table 8 Eigenvalues for first fivecomponents

Measures Dim. 1 Dim. 2 Dim. 3 Dim. 4 Dim. 5

Eigenvalues 0.032 0.026 0.023 0.020 0.020

% Variance 18.23 14.34 12.70 11.46 11.00

Cumulative 18.23 32.57 45.27 56.72 67.72

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

-0.2

0.0

0.2

Dim 1 (18.23%)

Dim

2 (1

4.34

%)

2005

2006

2007

2008

2009

2010

2011

20122013

2014

emotionalfit

manufacturers

models regulatory

retailershopping

competitive

customer

firm

market

model

satisfaction

trust

brand

loyalty returnsrisk stock

content

food

group

media

reveal

reviews

self

social

spendingdemonstrate

website

mobileratings

online

shoppers

networks

Fig. 5 Visual representation of years and words (MFACT)

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This draws our attention to the radical change in words that emerged during period

three, in contrast to the previous periods. It is clear that authors focused their attention on

contemporary issues, including the proliferation of online marketing and social networks.

Table 9 presents the characteristic words according to each analysed period. In the first

period (2005–2007), we identified words such as ‘fit,’ ‘manufacturer,’ ‘regulatory,’ and

‘model’ among others. The second period is characterized by the words ‘firm,’ ‘stock,’

‘loyalty,’ ‘efficiency,’ ‘competitive,’ ‘risk,’ and others. Finally, the third period gathered

words such as ‘media,’ ‘social,’ ‘network,’ ‘customer,’ and ‘mobile.’ These results are

consistent with the work proposed by Karvanen et al. (2014), who observed the growing

relevance of social media in contemporary marketing research.

Finally, in Fig. 6, the periods are shown again. Rather than highlight just those words

that characterize each period, each main topic is included in this visualization. For

instance, topics including regulatory issues, emotional aspects, and fit models shape the

first period. The second period features topics of consumer satisfaction and trust, firm risk,

and stock returns. The most recent period is made up of topics such as social media, food

reviews, food studies, online consumers, and mobile technologies. In this form, the

investigated period was accurately clustered into smaller ones by considering content

similarities of each abstract included in the analysis.

Discussion and limitations

In this research, a collection of 1169 abstracts from over the course of a decade was

investigated by proposing novel forms of applying classical statistical methods. All

abstracts correspond to articles that the most prestigious journals in the field have pub-

lished (JM and JMR). First, basic descriptive statistics of average words per abstract, the

percentage of unique terms, and average word length were provided. Thereafter, the most

frequent words were identified and allowed us to disclose the authors’ preferred vocabu-

lary. By conducting a correspondence analysis, the most influential abstracts were iden-

tified. Finally, a multifactor analysis of contingency tables was calculated to disclose how

the use of vocabulary has evolved. Three important periods that characterize how

vocabulary has evolved over time were disclosed.

This analysis gives evidence to the importance that authors have put on customer issues.

That is, the consumer was the center of marketing research during the investigated decade.

Similarly, the term ‘product’ comes next in importance. This is obvious, considering that

Table 9 Characteristic words by period

Period Characteristic words

2005–2006 Fit, manufacturer, regulatory, model, aversion, net, web, retailer, relationship, satisfaction,article, price, intention, author, emotional, structural, enhanced, shopping, involvement,relational, bias, parameter, reference, retailing

2007–2009 Firm, stock, loyalty, efficiency, competitive, risk, finance, customer, promotion, investments,market, chain, corporate, trust, manager, duration, valuation, revenue, shares, benefits,improvement, industry, impact, equity, scholars, interface, competitors, costs, marketing

2010–2014 Media, social, consumer, group, spending, rating, reveal, demonstrate, user, line, review,product, sale, food, content, online, advertising, network, employee, goal, position,campaign

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Scientometrics (2018) 117:293–312 309

199

marketing practices are almost meaningless without at least one product. The word ‘client’

does not appear in the top rank, but its presence increases in the third and last period, which

is unsurprising, as client and consumer are the same in most cases. The word ‘effect’ also

warrants attention because marketing science is having an effect on organizations and

people. Finally, the word ‘brand’ draws our attention because it is one of the foundations of

contemporary marketing.

This case study provides value for academics, researchers, and practitioners within the

marketing science area by tracking and identifying the most relevant publications with

respect to periods of time and topics. By providing easy-to-read visualizations, readers can

promptly identify those articles that made significant contributions in the field or locate

specific publication niches. This work also illustrates how literature reviews in marketing

can be effectively conducted while also reducing time spent. The main topic, ‘customer

choice,’ plays a strategic role in establishing a link between the consumer and purchasing

decisions. Two additional primary topics of interest are ‘developing strategies’ and ‘pro-

grams of pricing.’ This lends supporting evidence to the idea that pricing policies are

relevant to contemporary marketing, considering that pricing policies encompasses con-

cepts as ‘action indicators,’ ‘performance measures,’ and ‘profitability metrics.’ Our results

provide partial support for the popularity that Customer Relationship Management (CRM)

has gained in recent years. In this respect, topics most related with CRM are ‘added value,’

‘orientation,’ and ‘service.’ Here, the importance of having long-term relations with cus-

tomers, which is a core concept in marketing science, is also highlighted. ‘Emotional

marketing’ is another main topic that recognizes the generation of knowledge in this

discipline by investigating individuals’ emotions.

This work also contributes to the discussion of how literature reviews can be performed,

within marketing science or in other disciplines. Our primary goal was to propose useful

methods for classifying publications according to content similarities. The methods pre-

sented here might be used as general guidelines for authors and researchers who are

Fig. 6 Periods of evolution for the vocabulary in the first MFACT plane

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interested in performing literature reviews in a systematic way. By identifying the spe-

cialized vocabulary that is used in this discipline and later incorporating it into their

documents, authors may be assured that they are at the forefront of modern vocabulary

usage.

Text mining is an emerging discipline. As such, there are still some significant limi-

tations. Taking into account that only 1169 abstracts were incorporated in this study, our

results are more illustrative than truly generalizable. Therefore, we are not providing

compelling evidence about one accurate ‘radiography’ of marketing science; our work is

much more modest. Rather, the main objective was to demonstrate the suitability of text

mining techniques for conducting precise and standardized literature reviews. A broader

investigation should include the full text of each article to improve the accuracy of these

results. Moreover, categorical variables such as research center, country, and keywords

should be incorporated to better describe the ideal profile of the authors. A second paper,

which effectively incorporates these ideas, is currently in progress.

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foods

Article

Testing Model of Purchase Intention for Fast Food inMexico: How do Consumers React to Food Values,Positive Anticipated Emotions, Attitude toward theBrand, and Attitude toward Eating Hamburgers?

Héctor Hugo Pérez-Villarreal 1,2,* , María Pilar Martínez-Ruiz 1 and Alicia Izquierdo-Yusta 3

1 Faculty of Economics and Business Studies, University of Castilla-La Mancha, 02071 Albacete, Spain2 Engineering and Business Postgraduate Center, Popular Autonomous University of Puebla State,

72410 Puebla, Mexico3 Faculty of Economics and Business Studies, University of Burgos, 09001 Burgos, Spain* Correspondence: [email protected]

Received: 13 July 2019; Accepted: 21 August 2019; Published: 27 August 2019��

Abstract: This research investigated the effect of the food values, positive anticipated emotions,attitude toward the brand, and attitude toward eating a hamburger on purchase intention in fast-foodrestaurants in Mexico conjointly. The purpose of this study was to discover which variables influencedthe consumer´s intention to buy. Data was collected from a survey of 512 Mexicans fast-foodconsumers. Structural equation modeling was used to test the hypothesized associations. The resultsshowed that food values and positive anticipated emotions absolutely impact the attitude toward thebrand, which impacts the purchase intention of the Mexican consumers. Nonetheless, the positiveanticipated emotions impact stronger than food values, and the best way to get a purchase intentionis toward the attitude of the brand rather than attitude toward eating a hamburger. The authorsdiscussed inferences and suggestions for consumer approaches.

Keywords: food values; positive anticipated emotions; attitude toward the brand; attitude towardeating a hamburger; purchase intention

1. Introduction

Food choice decisions are complicated when every day the consumers make a lot of decisionsabout one excellent fast food [1]. Over the past few years, some studies have had a primordial objectiveto explain how interaction facts affect purchase intention through theory planned behavior (TPB) [2–4].However, none focused on the food values, especially when the research was about food choice andpositive anticipated emotions like a central variable in the model. Based on a dataset of 1169 abstractsof marketing from 2005 to 2014, Barahona et al. (2018) [5] explained that one crucial dimension forresearchers is emotional marketing. Topics such as evaluation, experience, message, people, emotional,goal, and hedonic are the keywords for studies in this field. Therefore, this research was based on thepurpose of explaining the purchase intention in four main premises. First, fast food consumption has apurchase intention by the attitude toward the brand into the means of an emotional need according toa physiological desire [6–8]. Second, the consumers´ emotions influence the purchase intention [9].Third, what is the role of food values on attitude toward the brand and attitude toward eating ahamburger [10]? Fourth, what is more essential to predict the purchase intention: attitude toward thebrand or attitude toward eating a hamburger [11]?

Through this research, a model with these variables was proposed because there is a synergisticeffect between them. The approach rests with the effects of food values and positive early emotions

Foods 2019, 8, 369; doi:10.3390/foods8090369 www.mdpi.com/journal/foods

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Foods 2019, 8, 369 2 of 17

directed towards the form of the attitude as a predecessor of the purchase intention [12–14]. This modelwas designed from the separation of attitudes: one directed towards the act of eating and anothertowards the brand. The application covers the principle on attitudes directed towards the product andanother towards the brand. Thus, this model is the first that uses the rational and emotional part ofconsumption and separates the attitude of eating from the attitude towards the brand. In this case,the model provides information on the importance of the product and the brand and towards launch,modifications and valuations of products and brands. The consumer’s decisions are based on somelevel of rational or emotional effect [15,16].

This study forms the rational (food values) and emotional (positive anticipated emotions) parts toconnect them with different attitudes to predict purchase intention. Consequently, it used these twoattitudes roles, eating versus brand, to test the relationship to purchase intention. The importance ofthe study is to predict the purchase intention and to know the consumers’ behavior choices with ahamburger. If the calculations, weights, loadings, etc. contribute to explaining more of the purchaseintention, it should make an important and significant contribution to academic literature. This isbecause it gives off too many forms to investigates and implement strategies in fast-food restaurants,knowing the protrusion factors in the model.

For these reasons, it is intended to identify which emotions, food values and types of attitudesimpact significantly and positively on the purchase intention. Through these findings, marketingstrategies can be formulated and it is possible to know what the most convenient way for this field is.The objective of the present study was to explicitly test the purchase intention toward attitudes, foodvalues and positive anticipated emotions. The study built a model on purchase intention research byexamining the consumer before the purchase decision. Also, this study emphasized the meaning of therole of attitudes (eating hamburger and brand) on purchase intentions of fast food consumers. Finally,the study tested and confirmed the hypotheses planted in this research.

1.1. Attitudes in Consumer Behavior

Attitude toward something is an antecedent of intention, but it is also the degree to which anindividual has a favorable or unfavorable evaluation or appraisal of the behavior to any purchasesituation [17]. Some research has also highlighted the role of purchase intention and the attitudeimpact [18]. On the other hand, the attitude that is formed in the first stage is formed of the decisionprocess of purchase in the consumer (recognition of the need/problem). Some studies proved thatthe attitude directly affects the consumer’s buying behavior [19–21]. This attitude is influenced byelements such as information, nature of the product, social media, ads and other behavioral factors. Inthe context of food consumption, the role of attitudes is at the top for research in consumer behavior.Thus, some consumers have attitudes toward eating hamburgers and others have attitudes toward thebrand. This is because they keep both positive and negative evaluations, such as purchases intentions,purchases and repurchases [22]. However, in marketing as a discipline, the gap is different betweenattitude toward eating a hamburger and attitude toward the brand.

Attitudes toward eating hamburgers play a significant role in understanding consumer behavior.These attitudes can be decision-making components for the choice and intention to eat some food [23,24].Once consumers recognize their need for food, they enter into a stage of searching and evaluating thealternatives [25]. It is at this stage, where people positively or negatively value the desired behaviorwithout implying the degree of eating habits or the level of hunger [26]. Hence, the attitude of eatingevaluates the favorable or unfavorable predisposition towards the act of eating any food [17]. Rezai etal. (2017) [27] pointed to a direct relationship between attitudes towards eating foods that generate ahealthy benefit and the intention to buy. For this reason, it is vital to know one’s attitude towards theact of eating as a central point towards the intention to buy.

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On the other side, attitudes are cognitions and can sometimes be directed towards the brand [28].So it is necessary to comment that attitudes towards the brand can generate a behavioral intent andthe same behavior of the consumer’s final purchase [29]. Therefore, attitudes towards the brandmean that consumers adopt or reject conduct based on experiences, personal recommendations andmedia exposure, as well as other media that use the brand and may have a point of contact with theconsumer [30]. Hence, attitudes towards the brand have become one of the intangible componentsvalued by consumers because when choosing the behavior, they do it more for the brand than forthe product. Similarly, the attitude towards the brand makes consumers acquire feelings of security,confidence, convenience, and credibility among others, so for them, it is easier to recognize and choosethe purchase [31]. Thus, the literature agrees that attitude towards the brand is the highest pointthrough which the consumer disseminates the choice.

1.2. Purchase Intention

Assael (1998) [32] called purchase intention the conduct that seeks in response to an object andis before the purchase. Subsequently, Zhang et al. (2018) [33] approved the relationship betweenattitudes and purchase intention. Phau and Teah (2009) [34] demonstrated that when the consumerhas a strong positive attitude, there is a higer intention to buy.

Rezai et al. (2017) [27] pointed out the importance of determining the intention to purchasefunctional products from examining the factors involved in the purchase decision process. For example,Jahn, Tsalis, and L’hteenm-ki (2019) [35] indicated that the general attitude towards products has adirect effect towards the intention to purchase, as long as the people are in a condition of suitabilityand knowledge of the problem. Asif et al. (2018) [36] pointed out that it is possible to find differencesin intent to buy from one country to another, but they agreed that attitude and health awareness arethe best predictors of the intention to buy in organic foods. Some studies pointed to some additionalvariables to the TPB including moral attitude and healthy awareness towards purchasing intent inorganic foods [37]. Consequently, it is possible to include other variables in the purchase intentionby extending the TPB. On the other hand, another study pointed to the involvement towards theconsumption of products, price sensitivity and moderation of the effect of the identity of the localproduct towards the intention of purchase [38].

Chiu, Hsieh, and Kuo (2012) [39] and Diallo (2012) [40] underlined aspects about the probabilityto buy, not before the consumer formed an attitude and experience of the past. Now, as the intention istestified to be a significant factor of buying, it was thus, hypothesized that:

Hypothesis 1 (H1). Attitude toward the brand will positively influence intention to buy.

Hypothesis 2 (H2). Attitude toward eating hamburger will positively influence the intention to buy.

1.3. Food Values

The situation of obtaining information on the attributes of the product has always been arelevant topic in food consumer research. Today, exotic consumption attributes, towards the ethics ofconsumption, healthy awareness, animal impact and organic food are topics of interest in knowing one’sbehavior [41–44]. According to Basha and Lal (2019) [45], the ratio of environmental concern, healthand lifestyle, supporting local farmers, product quality, convenience, price, animal welfare, safety-trust,subjective norms, and attitude is valued. The food choice has been becoming an advantage to improvehealthy and sustainable diets and to know the different roles of high and low involvement [46].Nevertheless, Boer and Schösler (2016) [46] mentioned that the differences in the affinities could bepredicted by food-related value motivation.

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Sprotles and Kendall (1986) [47], through consumer styles inventory (CSI), claimed that consumerschoose to make their purchase decision through eight basic styles: high quality, innovation, brandawareness, price, hedonism, confusion with other brands, impulsivity, and habit. Other studiesemphasized product presentation, food safety, environmental impact, and ethical consumer identity [48].Another study found that depending on the type of food (organic or conventional) used, the effect onthe consumer perception component (e.g., healthy consciousness) differs [49].

When researches talk about the food attributes, it can be partial to the real concept because thefood attributes can be an infinite number of characteristics, but only some of them are important forthe moment of choice [50]. For this reason, the attributes of the product became the consumer’s valuesregarding food. Some researchers affirmed that these values were influenced through many factors,which relate to personal values [1,51–53]. This means that food values are exercised by the consumerand not by the product itself. However, each attribute mentioned above falls within a factor of the11 described by Lusk (2011) [54]. Thus, it is possible that each product, depending on belonging in thecategory, constitutes intra-group differences, but it is possible to categorize them in general forms.

Lusk and Briggeman (2009) [55] explored all the factors that integrated the attributes of food.After this plan, Lusk (2011) [54] opened wide 11 items to identify the food values scale. These itemsare (1) naturalness (the extent to which food is produced without modern technologies), (2) taste (theextent to which consumption of food is appealing to the senses), (3) price (the amount paid for food),(4) safety (the extent to which consumption of food will not cause illness), (5) convenience (the easewith which food is cooked and consumed), (6) nutrition (the amount and type of fat, protein, vitamins,etc.), (7) tradition (preserving traditional consumption patterns), (8) origin (where the agriculturalcommodities were grown), (9) fairness (the extent to which all parties involved in food productionequally benefit), (10) appearance (the extent to which food looks appealing), and (11) environmentalimpact (the effect of food production on the environment).

Studies have shown that food values are essential to explain attitudes. For example, Manan(2016) [1] emphasized to know the attitudes through personal values, but the question is whetherpersonal values are influenced by the food benefits, if that correct, then these affect attitude. In order,Lang and Lemmerer (2019) [53] demonstrated the relationships across personal values and attitudestoward local food, but they did not separate the attitude toward eating a hamburger or the attitudetoward the brand. As a result, it is hypothesized that:

Hypothesis 3 (H3). Food values will positively influence attitude toward the brand.

Hypothesis 4 (H4). Food values will positively influence attitude toward eating a hamburger.

1.4. Anticipated Emotions

Some researchers have been in charge of framing emotions as a fundamental, principal axis anddetonator of all purchasing behavior, this adding to the part of information processing and consumeraction [56–62]. Although the entire chain of observation (cognitive, conative and affective), the triggerand the key factors of success cannot be established, some researchers have taken a part of the chaintowards the effective and successful verification of the application of branding emotional, buyback,purchase decision, search, and evaluation of purchase alternatives [63–66].

Within the contributions of advertising, it is possible to highlight that the emotional contagionmay have main effects on the physiological changes of the people [67]. In this study, the participantsfelt sadder when they saw a victim with a sad face, and their sadness emanated the effect on theexpression of the emotion in the sympathy. The effects of contagion are automatic and not inferentialbut are diminished by deliberative thinking. On the other hand, Nielsen et al. (2010) [68] showed thatthe “pre-attention” processing of semantic information in non-focal announcement titles can provokeorientations towards attention responses. The same results were in foreseeable increases in the ad and

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knowledge of the brand. Equally, Teixeira et al. (2012) [59] showed that surprise and joy concentrateeffective attention and retain the viewers with more time. However, the most important thing is thelevel of retention instead of the speed of surprise, and it affects the concentration of attention more.Therefore, speed influences the level of joy, which affects spectator retention. These three studiesplaced the emotional part as the main factor in their research with the impact on advertising. It couldbe specified that the authors discussed the implications of the use of emotional expressions, titles ofadvertisements, and consumer knowledge of the brand to promote emotions in the consumer and helpthe purchasing decision process.

However, the emotions are present throughout the process of consumer behavior, but it is vitalto determine what the origin of this is. Pelsmaeker et al. (2017) [69] explained the relationship ofemotions in the begging of the process of consumer intention, and they determined the relevance ofapplying an evaluation before recognizing the need. Emotions can indeed be positive and negativedepending on the moment or value. However, some researchers in recent years were working onlyfor positive emotions because only these matter. Wen, Hu and Kim (2018) [70] examined the effect ofindividual culture on positive emotions for the recommendation intention. Finally, positive emotionsare the principal element to determine the satisfaction of the consumer [71].

Williams and Aaker (2002) [72] believed that when individuals are exposed to mixed emotions,they influenced the individual´s attitudes in general. They also demonstrated that the detonation ofemotions with duality (e.g., sadness and happiness) is less prone to form an attitude towards theirbehavior. Haws and Winterich (2013) [73] described the factors to measure the attitude toward eatingdirectly to these items: pleasure, enjoy, satisfied, and good taste. However, the consumer can have anattitude toward the brand and not for eating. That reason describes Aggarwal and Mcgill’s (2012) [74]finding that what consumers like, think, admire, and fit in their life is a good positive attitude thathelps to stimulate the intention. This study proposed two constructs, one for eating the hamburgerand the other for the brand.

Thus, the following hypothesis can be derived:

Hypothesis 5 (H5). Positive anticipated emotions will positively influence attitude toward the brand.

Hypothesis 6 (H6). Positive anticipated emotions will positively influence attitude toward eating a hamburger.

Hypothesis 7 (H7). Positive anticipated emotions will positively influence the intention to buy.

Therefore, seven hypotheses were tested in this research and based on the discussion above (seeFigure 1), and considers seven proposed effects: (1) attitude toward the brand on purchase intention,(2) attitude toward eating hamburger on purchase intention, (3) food values on attitude toward thebrand, (4) food values on attitude toward eating hamburger, (5) positive anticipated emotions onattitude toward the brand, (6) positive anticipated emotions on attitude toward eating hamburger,and (7) positive anticipated emotions on purchase intention. Thus, all the effects correspond to a newmodel for understanding better the purchase intention in fast-food restaurants.

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Figure 1. Model development.

2. Materials and Methods

This study utilized partial least squares-structural equation modelling (PLS-SEM) to examine theimpact of the food values, emotions anticipated and attitudes on purchase intention (see Table 1 fortechnical details). The proposal was to estimate a model that includes a mix of factors and compositesusing the PLS algorithm procedure [75]. The idea was to maximize the explained variance of alldependent variables used in the research model. In this case, the research intent was to know thepredictor variable and to identify possible drivers [76,77]. Therefore, the independent variables thatthe literature reports as important predecessors of purchase intention were also included.

Table 1. Technical Details.

Universe Residents in Puebla State in México

Sample unit People over 17 years old and buying fast food

Information collection method Personal survey

Sample error ±4.335




Period of information collection January 26–May 23 (2018)

Language Spanish

2.1. Data Collection

The data was collected from Puebla City in Mexico with a consumer survey of 512 participants.Participation was voluntary and all of them completed the questionnaire.

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2.2. Statistics Analysis

The study used structural equation modeling (SEM) to test the conceptual model with SmartPLS3.0 software. According to Streukekens and Leroi-Werelds (2016) [78], this study used partial leastsquares (PLS) with a 10,000 subsample bootstrapping procedure and the same software to know if therelationship was supported or not with the results. In the beginning, this model was composted from34 items reduced to 28 items in five constructs. From there, no preliminary empirical parameters forthis particular market were found.

2.3. Questionnaire Development

The questionnaire was constructed and divided into five sections: (a) food values, (b) positive andnegative anticipated emotions, (c) attitude toward the brand, (d) attitude toward eating a hamburger,and (e) purchase intention (see Table 2). The first table shows the questionnaire section by source andthe second explains details on how to measure each variable.

Table 2. Questionnaire sections.

Latent VariableObservedVariables

Definition Source

Food values aregeneral food

attributes thatconsumers believedwere relatively more

important whenpurchasing food

Appearance Extent to which food looks appealing

Lusk (2011) [54]

Convenience Ease with which food is cooked and consumed

Environmental Effect of food production on the environment

Fairness The extent to which all parties involved in theproduction of the food equally benefit

Naturalness Extent to which food is produced withoutmodern technologies

Nutrition Amount and type of fat, protein, vitamins, etc.

Origin Where the agricultural commoditieswere grown

Price The price that is paid for the food

Safety Extent to which consumption of food will notcause illness

Taste Extent to which consumption of the food isappealing to the senses

Tradition Preserving traditional consumption patterns

Positive and negativeanticipated emotions

Contentment If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel contentment

Adapted fromBagozzi and

Dholakia (2006)[79]

Delighted If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel delighted

Excited If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel excited

Proud If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel proud

Satisfied If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel satisfied

Selfassured If I can go to eat a hamburger in fast-foodrestaurants the next month, I feel self-assured

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Table 2. Cont.

Latent VariableObservedVariables

Definition Source

Attitude toward thebrand (ATB)

ATB1 Like the brandAggarwal and

McGill (2012) [74]ATB2 Admire the brand

ATB3 Fit in your life the brand

Attitude towardeating a hamburger

(ATEH)

ATEH1 Eating the hamburger would be pleasurable

Adapted fromHaws and

Winterich (2013)[73]

ATEH2 I would enjoy eating the hamburger

ATEH3 If I eat a hamburger, it would be satisfyingfor me

ATEH4 If I eat a hamburger because of the good tasteit has

Purchase intention

PI1 You probably buy products infast-food restaurants

Adapted fromChiu, Hsieh, andKuo (2012) [39],

Diallo (2012) [40]

PI2 I would consider buying a product in fast-foodrestaurants if I need a product of this type

PI3 It is possible to buy a product infast-food restaurants

PI5 The probability that you consider buying infast-food restaurants is high

The food values utilized a Likert scale 1–5 (1 = not at all important, to 5 = extremely important).The scale was adapted from 7 points to 5 points, because it was planned to explain each item as aformative construct. It is better to get an answer from the consumer on the assumption that some itemsdo not have a relation with the construct. Positive and negative anticipated emotions applied a Likertscale 1–7 (1 = none, to 7 = severe). From the original items, it supported the positive emotions becausethe negatives did not have an impact and did not comply with the test of validity and reliability. Itdeleted the emotions for: glad, relief and happy for the reason to have multicollinearity and the VIFfactor > 3.2. Also, it used the 7-point Likert scale as the author marked it. According to Becker andIsmail (2016) [80], it is possible to use different Likert scales within the same model. In the attitudetoward the brand (ATB), it used a Likert scale 1–5, (1 = strongly disagree, to 5 = strongly agree). Fromthe original contribution, it supported only the positive items because the weights were weak (item 4“shame” and 5 “avoidance”). It changed the inverse items for the nature of the scale. For the attitudetoward eating a hamburger (ATEH), it was handled with a Likert scale 1–5, (1 = strongly disagree, to5 = strongly agree). These items were adapted to the specific product (in this case, hamburger). Thevariable purchase intention was measured by a Likert scale 1–5, (1 = strongly disagree, to 5 = stronglyagree). PI4 was excluded because it had multicollinearity with PI3. The item was “I would buy in fastfood restaurants next time”.

All the constructs were reflective, not including food values. The construct formed theinterpretations depending on the dependent variable. Hence, the formative indicators may show up asnon-significant. Also, the indicators were correlated with other indicators in the model proposal [81].Similarly, all the formative indicators required a census of all items for the construct because each one(it can be negative or positive) was formed into a complete variable. Even the negative influences onthe consumer were one item that needed to be taken care of [82]. Finally, the overall fit of this modeldoes not matter; the other covariances like the exogenous variables are outside the model proposal, andall the items are independent of themselves, according to Jarvis, MacKenzie and Podsakoff (2003) [82].

3. Results

The development model was constructed on an amalgamation of items, concepts, models, effectsand principles about two parts: functional and emotional. This model was also composited about aseries of research studies around four exceptional areas: (1) food values, (2) attitude toward the brand,

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(3) attitude toward eating a hamburger, and (4) positive anticipated emotions. All were within theproposal to better explain the purchase intention in fast-food restaurants in Mexico.

To assess the goodness of model fit, the root mean square residual (SRMR) was utilized. Accordingto Hu and Bentler (1998) [83] and Hu and Bentler (1999) [84], SRMR < 0.08 is a good fit for SRMR. Thismodel has an SRMR = 0.049 < 0.08 SRMR criteria; these measures found that this model has a good fitwith the parameters mentioned before. The normed fit index (NIF) results in values from 0 to 1, andthe closer to 1, the better the fit [85]. In this model, the NIF was 0.899 and represented an acceptable fit.

To get confidence in this model, reliability and construct validity testing were carried out.Cronbach’s alpha coefficient was accepted for all the constructs, having a value greater than 0.7 [86].The rho_A value was reflected regularly if this index was larger than 0.7 [87]. The composite reliability(CR) values under 0.6 indicated a deficiency of internal consistency reliability [88]. The AVE of eachconstruct was above the tolerability value 0.5 [89,90] (see Table 3).

Table 3. Validity Testing.

Cronbach’s AlphaCoefficient

rho_AComposite

Reliability (CR)Average VarianceExtracted (AVE)

Attitude towardeating a hamburger 0.847 0.862 0.897 0.687

Attitude towardthe brand 0.822 0.836 0.893 0.736

Positive anticipatedemotions 0.916 0.921 0.934 0.704

Purchase intention 0.895 0.896 0.927 0.760

As a final point, the discriminant validity of constructs showed the factor loading indicators on theassigned construct. Therefore, they had to be above all loading of other constructs (in the same column)with the condition that the cut-off value of factor loading was higher than 0.70 [89]. In addition, themodel proved to have satisfactory reliability with convergent and discriminant validity. After thisstep, it was necessary to test the discriminant validity of constructs. According to Fornell and Larcker(1981) [89], with the correlation coefficient of the two dimensions less than the square root of the AVE,two dimensions were understood to have discriminant validity because of AVE > 0.5 (see Table 4).

Table 4. Association Testing.

Attitudetoward Eatinga Hamburger

Attitudetoward the

BrandFood Values

PositiveAnticipated

Emotions

PurchaseIntention

Attitudetoward eating a

hamburger0.829

Attitudetoward the

brand0.538 0.858

Food values 0.431 0.444 Formative

Positiveanticipatedemotions

0.482 0.544 0.401 0.839

Purchaseintention 0.537 0.665 0.407 0.544 0.872

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The study confirmed the hypothesis with path coefficient, standard error, t-value, and p-value(see Table 5). It was concluded that all the hypotheses planted were supported and positive to predictthe purchase intention with a high level, even though the study observed some differences about eachassociation. The first force is the association between attitude toward the brand on purchase intentionhad the best path coefficient (β = 0.447). Moreover, the results showed that attitude toward eating ahamburger was also important to purchase intention (β = 0.197). However, the other association topredict purchase intention was throughout the positive anticipated emotions and for this model was(β = 0.206), more than attitude toward eating a hamburger.

Table 5. Hypothesis Testing and Path Coefficients.

Beta Standard Error t-Value p-Value f 2 q2 Supported

H1Attitude toward thebrand -> Purchase

intention0.447 *** 0.041 10.849 0.000 0.249 0.134 Yes

H2


-> Purchaseintention

0.197 *** 0.043 4.574 0.000 0.053 0.030 Yes

H3Food values ->

Attitude toward thebrand

0.270 *** 0.042 6.447 0.000 0.095 0.050 Yes

H4Food values ->


0.284 *** 0.043 6.608 0.000 0.097 0.052 Yes

H5

Positive anticipatedemotions ->

Attitude toward thebrand

0.436 *** 0.043 10.126 0.000 0.248 0.146 Yes

H6

Positive anticipatedemotions ->


0.368 *** 0.040 9.167 0.000 0.163 0.088 Yes

H7Positive anticipated

emotions ->Purchase intention

0.206 *** 0.050 4.129 0.000 0.057 0.030 Yes

Note: n = 10,000 subsamples; *** p < 0.001; R2 (Attitude toward the brand = 0.357; Attitude toward eating = 0.300;Purchase intention= 0.515); q2 = Predictive relevance calculated ((R-Sq included)-(Q-Sq excluded))/(1-R-Sq included).

The great force to constitute the attitude toward the brand was with the construct positiveanticipated emotions (β = 0.436). Because, in comparison, the attitude toward eating a hamburger onlyhas β = 0.368. Something relevant was the impact of food values to the attitudes, where it had someconsideration to attitude toward eating a hamburger (β = 0.270), in contrast to the brand, where washigher (β = 0.284).

Some reflections about all the hypotheses proposed are the level of significance, where p-value<0.001 with the 99%; it means that these study results were statistically significant.

Also, the H5 line of positive anticipated emotions to attitude toward the brand (β= 0.436, t = 10.126,p = < 0.001) and the H1 line of attitude to purchase intention (β = 0.447, t = 10.849, p = <0.001) indicatedan abundant positive effect to form the purchase intention; this was the best way to predict it. Table 5shows that in all the relations, t-value ≥ 1.96 and p-value ≤ 0.05; thus, this model supported allthe hypotheses with high path coefficients and t-values. Hence, outer model loadings were highlysignificant. In addition, f2 was utilized to confirm the hypotheses null in the model and the outcomessupported each hypothesis but with different effects from weak <0.15 to large >0.15 [91]. All q2 areabove zero, which supports the model presenting in Figure 2 [88].

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Figure 2. PLS analysis results.

Esposito et al. (2010) [92] stated that formative constructs need not be correlated between them.Also, the construct needs to be supported with the theory about food values. Similarly, the PLSalgorithm produced loadings for reflective construct and weight for formative. Moreover, the studyused the loadings and weights indicator for each construct by nature.

Figure 2 indicates the formative construct (food values), and inside the construct, the best itemsare taste and tradition (0.490; 0.380). On the other hand, the food values show negative loading withenvironment and nutrition (−0.256; −0.233). These facts do not have a position for the food value. Also,the model indicates that the emotions of contentment, excited and satisfied are the best loadings in themodel (0.869, 0.856, 0.843).

It is distinguished that R2 (ATEH) is 0.357 higher than ATB (0.300). Additionally, R2 (PI) is 0.515,signifying that both attitudes toward eating and the brand plus positive anticipated emotions explain51% of purchase intention. Even though R2-ATEH and R2-ATB are weak, the R2-purchase intention issubstantial [91].

4. Discussion

All the hypotheses proposed were supported and confirmed. It accepted the difference by twotypes of attitudes: one of them toward the brand and the other toward eating a hamburger. Also, itshowed the gap between the beta indicators with 0.250 to predict the purchase intention. The attitudetoward the brand got first place in the hypotheses. Based on the previous study, the theory andempirical research suggested that attitude toward the brand will positively influence the intention tobuy. After the results, it confirmed the positive influence and on the same road with other studies. Inthis case, it corroborated with the results of Hwang, Yoon and Park (2011) [29] which mentioned thatthe affective responses positively influence brand attitudes and purchase intention. The attitude towardeating had the right place in the final model. This hypothesis was confirmed, and the values obtainedhelp to explain, with a higher percentage, the purchase intention. Other authors affirm the importanceto investigate eating behavior to get knowledge about the positive or negative predisposition toeat [23,24]. The hypotheses related to food values were an essential variable in this model, i.e., therelationship of this variable to both attitudes. At this point, it is demonstrated that the food valuescould be impacted in a different way to each attitude. It validated the influence of food values affecting

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indirectly on the purchase intention. With this information, it led to some discussion to add more foodvalues and to get an effect indirect to purchase intention. For example, these results match to Lang andLemmerer (2019) [53] which affirm that personal values impact on forming a food attitude. Last, thepositive anticipated emotion positively influenced attitude toward the brand, attitude toward eating,and intention to buy a hamburger. The results are consistent with previous research, which assertthat emotion is an irreplaceable variable to try predicting the purchase intention. Positive anticipatedemotion is a significant variable, which participates in three hypotheses addressing attitude towardthe brand, attitude toward eating a hamburger and purchase intention. This confirms findings in otherstudies [74,93,94].

Managerial implications are confirmations derived from this research. First of all, managers offast-food restaurants have to focus on the purchase intention of consumers. The findings support thatpurchase intention is more influenced by attitude toward the brand than by attitude toward eatinga hamburger. Subsequently, the food values do not impact very strongly, but positive anticipatedemotions do. The managers need to study how powerful each emotion (contentment, excited andsatisfied) is before thinking about eating something at a fast-food restaurant. Also, the best values tobuild into the product are taste and tradition. Hence, in this case, the managers need to investigateabout preferences, tastes and culture around consumption in fast-food restaurants. In that way, theyneed to prefer a strategy with a focus to increase and improve the value of the brand toward the brandequity oriented into the consumer. Correspondingly, positive anticipated emotions do not have a goodassociation directly with purchase intention. This explains that without an attitude toward eating ahamburger or the attitude toward the brand, the consumer does not perceive the intention to buy ahamburger at a fast-food restaurant.

Limitations and Future Orientations

There are limitations and suggested future lines of research. First of all, the sample should beincreased to raise the level of confidence and lower the level of sampling error. Alternatively, it isrecommended to add other variables related to TPB as perceived control, perceived difficulty andsubjective norms on purchase intention. Finally, it is suggested to apply these surveys in other cities,products, and brands to know if there are significant differences between the samples.

5. Conclusions

The goal for this study was building a development and testing model, having one comprehensivemodel about the purchase intention. The study planted a model with the importance of functional andemotional aspects through their effects on two attitudes. This model is an approximation to betterexplain the purchase intention. The food values have a low position on attitude toward the brandand attitude toward eating a hamburger. On the other hand, anticipated positive emotions have morerelevance on attitudes, especially the attitude toward the brand and to purchase intention.

The positive food values are taste and tradition in fast-food consumers. This model providesinformation to fast-food restaurants to pay attention to constantly evaluate the taste that has theconsumers’ favor and to explore insights about a different perception of taste in the hamburger. Also,the tradition is significant because it includes and preserves traditional consumption patterns, sincechildren families and reference groups help to educate this kind of consumption. From the otherview, the consumer does not care about the nutrition of the hamburger against the knowledge ofthe brand. This confirms the results from Barone et al. (1996) [95] that examined the cause to formincorrect conclusions about the product. In this case, the consumer does not give value to the types offat, proteins, vitamins, and carbohydrates that the hamburgers have. This demonstrates the lack ofsensitivity and knowledge of healthy and responsible consumption.

Similarly, it is also happening with the environment value where the most significant weightin the variable of food value is. The consumer does not care if the burger is produced while takingcare of the environment. The problem of having production for the environment and pollution does

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not see some or any benefit knowing how the food was manufactured. So, the adequacy of practicesin favor of the environment and eco-friendly consumption is not significantly crucial for attitude orpurchase intention.

It was also shown that positive anticipated emotions form the best way to explain the purchaseintention. First of all, it was verified that the anticipated negative emotions did not show any relevantdata that included that variable within the model. Subsequently, the items with the greatest loadingswere analyzed, and the results were positive anticipated emotions like contentment, delighted, excited,proud, satisfied, and self-assured. If the consumer is to have one of these emotions, it is probably tohave a good level of attitude toward the brand and then to get a purchase intention.

For this reason, the results of the study confirm the existence of a strong relationship betweenattitudes toward the brand on purchase intention by way of anticipated positive emotions in theconsumer of fast-food restaurant. This proves, as in previous literature, that emotions are a necessarymeasure of the decision-making process of the consumer [96].

Author Contributions: Conceptualization, H.H.P.-V. and M.P.M.-R.; Methodology, H.H.P.-V. and A.I.-Y.; software,H.H.P.-V. and A.I.-Y.; validation, H.H.P.-V. and A.I.-Y.; formal analysis, H.H.P.-V., M.P.M.-R., and A.I.-Y.;investigation, H.H.P.-V.; resources; H.H.P.-V.; data curation, A.I.-Y.; writing—original draft preparation, H.H.P.-V.,M.P.M.-R., and A.I.-Y.; writing—review and editing, H.H.P.-V., M.P.M.-R., and A.I.-Y.; visualization, H.H.P.-V.;supervision, M.P.M.-R., and A.I.-Y.; project administration, H.H.P.-V.; funding acquisition, H.H.P.-V.

Funding: This research was funded by Universidad Popular Autónoma del Estado de Puebla (UPAEP), Sistemasde Información de Marketing: Sistemas de información, modelización y gestión para la toma de decisiones enMarketing and the APC was funded by UPAEP.

Conflicts of Interest: The authors declare no conflict of interest.

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219

Appendices

Appendice 4. Impact factor

Original article: Identifying research topics in marketing science along

the past decade: a content analysis.

Journal: Scientometrics

Impact Factor

2.77 2.71 2018 5 años

Categoría de JCR® Clasificación en la categoría Cuartil en la categoría

COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

41 de 106 Q2

INFORMATION SCIENCE & LIBRARY SCIENCE

20 de 89 Q1

Datos de la edición 2018 de Journal Citation Reports

Editorial: SPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT,

NETHERLANDS

ISSN: 0138-9130

eISSN: 1588-2861

Dominio de investigación: Computer Science

Information Science & Library Science

221

Doctoral Thesis


Original article. Testing Model of Purchase Intention for Fast Food in

Mexico: How do Consumers React to Food Values, Positive Anticipated

Emotions, Attitude toward the Brand, and Attitude toward Eating

Hamburgers?

Journal: Foods

Impact Factor

3.011 2018

Categoría de JCR® Clasificación en la categoría Cuartil en la categoría

FOOD SCIENCE & TECHNOLOGY

36 de 135 Q2

Datos de la edición 2018 de Journal Citation Reports

Editorial: MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

ISSN: 2304-8158

Dominio de investigación: Food Science & Technology

222